WEB ANALYTICS

Analysing, Classifying and Describing Web Metrics with Fuzzy Logic

Darius Zumstein

Information Systems Research Group, University of Fribourg, Boulevard de Pérolles 90, 1700 Fribourg, Switzerland

Keywords: Web analytics, Web controlling, Web metrics, Electronic business, Customer relationship management,

Fuzzy classification, Fuzzy logic, Computing with words.

Abstract: In the Internet economy, it has become a crucial task of electronic business to monitor and optimize web-

sites, their usage and online marketing success. Web analytics, which is defined as the measurement, collec-

tion, analysis and reporting of Internet data, is an effective instrument of website management. First, this

paper describes the technical functionality and use of web analytics and discusses different web metrics.

Second, a fuzzy web analytics approach is proposed, which makes it possible to classify metrics precisely

into more than one class at the same time. Third, a fuzzy web metrics index has been developed for multi-

dimensional, intelligent web analysis. Fuzzy logic enables computing with words and more intuitive, hu-

man-oriented queries, segmentation and descriptions of metrics in natural language. Finally, a web analytics

framework is suggested to analyze and control key performance indicators in a web controlling loop.

1 INTRODUCTION

Since the development of the World Wide Web 20

years ago, company websites have become a crucial

instrument of information, communication and elec-

tronic business. With the growing importance of the

web, the analysis, monitoring and optimization of a

website and online marketing, web analytics, is now

an important issue for business practice and academ-

ic research. Web analytics (WA) enables better un-

derstanding of the traffic and behavior of users on

websites, by analyzing different metrics and success

factors, i.e. key performance indicators (KPIs).

Today, many companies are using web analytics

software from providers like Google, Nedstad, Web-

Trends or Omniture to collect, store and analyze web

data. These tools provide dashboards and reports

with many metrics to web analysts and managers,

responsible for planning and decision-making about

website-related activities. One problem of measure-

ment-based reports is that all values, e.g. the number

of page views, visits, visitors or conversions, are

often raw numbers and therefore difficult to interp-

ret. Usually, they only make sense in comparison

with past values, target values or external values, or

if segmented by other metrics. Another problem is

that web data and metrics are usually reported, clas-

sified and evaluated in a sharp manner.

This paper proposes a fuzzy logic approach mak-

ing it possible to classify web data and metrics fuzzi-

ly and to analyze and express their values with mea-

ningful linguistic variables (i.e. words or word com-

binations). After an initial presentation of the func-

tionality and use of WA, different web metrics are

introduced in section 2. Section 3 explains and ex-

emplifies the fuzzy logic approach, showing how it

can be used for classifying, indexing, segmenting

and computing with words. Section 4 explains the

web controlling loop and section 5 offers a conclu-

sion and outlines likely future developments.

2 WEB ANALYTICS

2.1 Definition

According to the Web Analytics Association (2009),

web analytics is the measurement, collection, analy-

sis and reporting of Internet data for the purposes of

understanding and optimizing web usage.

Weischedel et al. (2005) define WA as the moni-

toring and reporting of website usage so that enter-

prises can better understand the complex interactions

between website visitor actions and website offers,

as well as leverage insight to optimize the site for

increased customer loyalty and sales.

282

Zumstein D.

WEB ANALYTICS - Analysing, Classifying and Describing Web Metrics with Fuzzy Logic.

DOI: 10.5220/0002778502820290

In Proceedings of the 6th International Conference on Web Information Systems and Technology (WEBIST 2010), page

ISBN: 978-989-674-025-2

However, it is not only web usage and online

sales which can be monitored, but other goals of a

web presence too. In this paper, therefore, web ana-

lytics is defined as the selection, definition, analysis

and evaluation of key performance indicators (KPIs)

and web metrics in order to verify the achievement

of website-based objectives.

2.2 Functionality of Web Analytics

Technically, in web analytics a distinction can be

made between five different approaches to collecting

data: the analysis of log files (server-side data col-

lection), page tagging (client-side data collection),

the use of packet sniffing, web beacons and reverse

proxies. The following paragraphs focus on log file

analysis and page tagging, as other methods are not

often used in research and business practice.

Server-side Data Collection. In this method, data

from log files are extracted and analyzed. Each time

a web page is loaded in a browser, data such as the

user’s IP address and the names of requested files is

saved with a time stamp in the log file of a web

server. The advantage of log file analysis is that all

requests and file downloads (text, PDF, picture or

video files) from a web server are logged. However,

one disadvantage of log file analysis is that traffic

on the site is not measured exactly because of the

caching in browsers and proxy servers. Additionally,

the requests from search engines, robots and craw-

lers distort the statistics. Moreover, visitors cannot

be identified distinctly, and user actions like mouse

clicks are not tracked either. Finally, the extraction,

preparation and analysis of log files can be complex

and time-consuming. Given these problems, log file

analysis has lost ground in recent years. Today, most

tools are using page tagging, or hybrid methods (us-

ing both server- and client-side data collection).

Client-side Data Collection. In this WA method, a

piece of JavaScript code is inserted in each HTML

page. If a page is loaded in the client’s browser, the

JavaScript is executed, a 1x1 pixel tag loaded, and

all data regarding the page view and the visitor’s

actions is transmitted to an internal or external track-

ing server. Using cookies, data about each user and

that user’s sessions are recorded.

Client-side WA solutions are mostly provided as

‘software as a service’ (SaaS) by application service

providers (ASP). They have many advantages: First,

all of the actions of each website user are recorded

in real time, i.e. all mouse clicks and all keyboard

entries. Technical information about users is cap-

tured too: the size, resolution and colors of the moni-

tor, type and language of the browser and operating

system, and all plug-ins installed. Second, there is no

caching in browsers or proxies, and the JavaScript is

not read by search engine crawlers and robots. Final-

ly, the tagging method can be implemented easily

and no IT specialists are needed. Despite data priva-

cy issues, the client-side data collection method has

become the standard method in web analytics.

2.3 Use of Web Analytics

The use of web analytics depends on the objectives

of a website. However, the main benefits are:

User and Customer Orientation. With WA the

tracking system reveals the information (pages, con-

tent, files) and services (searches, forms, blogs,

RSS) accessed by users. By analyzing information

demand, a high degree of user and customer orienta-

tion can be guaranteed, which is valuable for CRM.

Website Optimization. Based on an analysis of

each user’s information accessing behaviour, web-

sites can be adapted to their surfing, clicking, navi-

gation and search characteristics. Moreover, website

quality can be improved by testing and optimizing

the navigation, structure, links, functionality, usabili-

ty, design and content of the website.

Search Engine Optimization. WA is used to ana-

lyze and monitor search engine optimization (SEO)

and marketing (SEM). The goal of SEO and SEM is

to improve rankings in search engines, using certain

techniques. Web analytics tools reveal where users

are located (continent, country and place) and identi-

fy the search words used to find the web page.

Optimization of Online Marketing. Additionally,

WA can help to measure the effects of online mar-

keting instruments like banner advertisements, news-

letters, surveys and online campaigns.

Finally, WA facilitates rational decision-making,

and target- and performance-oriented management

of websites in order to improve e-business success.

2.4 Web Metrics

For WA, a number of metrics have been reported.

These are listed in Table 1 and appear in Figure 1.

The number of page views, visits, visitors and

the time on page are the standard metrics measured

by most tools and which have often been discussed

in the literature (see e.g. Sterne 2002, Peterson 2005,

Kaushik 2009). The main KPIs of web usage a

WEB ANALYTICS - Analysing, Classifying and Describing Web Metrics with Fuzzy Logic

283

Figure 1: Relations between important web metrics.

Table 1: Definitions of various web metrics.

Page views Number of page views (impressions

of a web page) accessed by a human visitor

Visits A sequence of page views (sessions)

requests of a visitor without interruption

Visitors Number of unique visitors (users) on a website

(excluding crawlers, robots, spiders)

Time on page Average length of time spent on a web page by

all visitors

Stickiness Ability of a web page to keep a visitor on the

website

Bounce rate % of single page view visits (users quit the page

immediately without further action)

Frequency # of visits a user has made to the website

Visit

recency

Number of days since a visitor’s last visit to the

site

Visit length Length of visit, the time visitors spend on the

website (in seconds)

Visit depth # of pages accessed by a visitor on a visit

Conversion

rate

Proportion of visitors (users, surfers) becoming

online customers (buyers)

Ad conver-

sion rate

Proportion of visitors clicking on a banner and

then making a purchase on the website

Display

click rate

Proportion of visitors viewing one or more prod-

uct/service pages

Click-to-

basket rate

Proportion of visitors putting one (or more)

product(s) in the shopping basket

Basket-to-

rate

Proportion of visitors paying for a product after

placing it in the shopping basket

Order

rate

Proportion of visitors ordering a product after

viewing the product page

Repurchase

rate

Proportion of online customers making repeat

purchases on the website

Purchase

recency

Length of time since the online customer’s last

purchase on the website

Purchase

frequency

Number of purchases made by an online custom-

er on the website for a certain period

Monetary

value

Monetary value, e.g. revenues, from an online

customer for a certain period

considered to be stickiness, visit frequency, length of

visit and depth of visit. If products or services are

offered in an online shop, additional metrics and

numbers of transaction are useful to analyze and

control electronic business and electronic commerce.

Which product pages were visited? Which prod-

ucts were put in the electronic shopping cart and

which were actually purchased? In order to optimize

pages, processes, e-shops or product mixes it is im-

portant to have the answers to these questions.

Finally, conversion and order rates, purchase

frequency and recency, revenues and profits are also

all KPIs of electronic commerce.

3 FUZZY WEB ANALYTICS

3.1 Fuzzy Classification of Metrics

The theory of fuzzy logic and fuzzy sets goes back

to Lofti A. Zadeh in 1965. It takes the subjectivity,

imprecision, uncertainty and vagueness of human

thinking and language into account, and expresses it

with mathematical functions.

A fuzzy set can be defined formally as follows

(1; Zimmermann 1992, Meier et al. 2008): if X is a

set, then a fuzzy set A in X (A

⊂ X) is defined as

()

(

)

{

}

iAi

Ax x

(1)

where x

∈ X, µ

: X → [0, 1] is the membership

function of A and µ

(x) ∈ [0, 1] is the membership

degree of x in A. In what follows, an illustration of

fuzzy web analytics is provided. In a sharp set (see

Figure 2a), the terms “few”, “medium” or “many” of

the linguistic variable (web metric) “page views”

can be either true (1) or false (0). A value of 1 of the

membership function µ (Y-axis in Figure 2a) means

that the number of page views (on the X-axis) corre-

sponds to one set. A value of 0 indicates that a num-

end:

Basket

Conversion rate

Order rate

Web metrics of

website usage

Order

Product page

Entry page

Web

Exit

engines

Bookmarks,

URL entries

Online

advertising

Ad conversion rate

Online revenues and

(e.g. per visit, visitor, order)

External

links

Stickiness

Web metrics of

e-commerce

umber of (new and

returning) customers

Clic

-stream & clic

ath

Depth of visit

Length of visit

(Visit) frequency



Click rate

Reach

Display

click rate

Click-to-

aske

Basket-to-buy

rate

umber of

e views

Average time on page

Number of visitors

Number of visits

Ad click

rate

Key performance indicators (KPIs) Web metric

Bounce Rate

Purchase

uenc

& recency

Metric ratio (rate)

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284

ber of page views does not belong to one of the sets.

In the illustration, the number of page views is de-

fined as “few” between 0 and 32, while 33 to 65 is

“medium” and more than 66 is classified as “many”.

If page 1 is visited 65 times, it is classified in the

“medium” class, while web page 2, with 69, has

“many” views. Although the two pages have been

visited nearly the same number of times, they are

assigned to two different sets. In contrast, when de-

fining fuzzy sets (Figure 2b) by membership func-

tions, there are continuous transitions between the

terms “few”, “medium” and “many”. In a fuzzy ap-

proach, the number of page views for page 1 is clas-

sified both as “medium” (0.55 or 55%) and as

“many” (45%).

Figure 2: One-dimensional sharp (a) and fuzzy (b) classi-

fication of the web metric page views.

Page 2 also has part membership in two classes at

the same time (60% for “many” and 40% for “me-

dium”).

Now, an additional web metric, the bounce rate,

can be considered. Web page X has a “low” bounce

rate (and is therefore "sticky") if visitors view at

least one other page (Y) after visiting page X. Page

X has a “high” bounce rate if visitors leave the web-

site immediately after viewing page X (e.g. by clos-

ing the browser window). As can be seen in Figure

3, the two metrics ‘page views’ and bounce rate’

define a two-dimensional matrix with four classes:

Class 1 (C1) is defined by a high bounce rate and

many page views, while the pages in C2 - "leaky

flop pages" - have high bounce rates and few views.

C3 represents “sticky top pages”, and pages in C4

are also sticky, but have few page views. Here too,

the sharp classification of web pages is problematic:

although web page C has almost the same values as

B, C is classified as a “leaky flop page”, and B falls

into the opposite group as a “sticky top page”.

Figure 3: Two-dimensional fuzzy classification of the web

metrics ‘page views’ and ‘bounce rate’.

If a web analyst wants to identify all pages in C3,

a sharp query will return page B and A, but not page

C, although B & C are in very similar positions. In a

fuzzy classification, sharp boundaries disappear and

pages can belong to more than one class. In a fuzzy

approach, pages like B and C in the middle of the

matrix, belong to four classes at the same time.

The basis for calculating the values for each class

is the γ-operator in equation (2). This algebraic

product operator, known as the “compensatory and”,

has been empirically tested by Zimmermann (1992).

()

i=1

∏

⎛

⎝

⎜

⎞

⎠

⎟

−

⋅ 1 − 1 −

()

i=1

∏

⎛

⎝

⎜

⎞

⎠

⎟

(2)

where x

∈ X, γ ∈ [0, 1] and µ

is the membership

degree between 0 and 1 in a class (x); m is the num-

ber of fuzzy sets A

, …, A

defined over the refer-

ence set X with membership functions µ

,…, µ

; γ is

a constant used to influence the degree of member-

ship in the classes. Here, γ is defined as 0.5. The

product is calculated with the membership degrees

of each class and their inverted values (1 - µ

(x)).

For example, the membership degree of page B

(in Figure 3) in class 1 is calculated as follows (3):

C1 = .58⋅ .47

()

1−.5

()

⋅ 1 − 1 − .58

()

⋅ 1 − .47

()

(

)

= .2726

(3)

Obviously, the definition of fuzzy sets allows grad-

ual ranking within classes, and as a result, more pre-

cise classifications of web metric values. In addition,

data can be classified without loss of information.

Fuzzy classifications of KPIs like revenues and con-

version rates are especially important, since their

values have far-reaching consequences for business.

few

medium

many

Web page 1

Web page 2

0 10 20 30 40 50 60 70 80 90

100

Page views

(per day)

Web

e 1

0.40

0.60

0.45

0.55

Web page 2

few medium

many

Page views

(per day)

)

0 10 20 30 40 50 60 70 80 90 100

Bounce rate

100% C2)



C1)

Leaky

top pages

C2)

eaky flop page

Page D

μ low

Page views (per day)

.50

e C

.49

Page

C4)

Sticky flop

pages

C3)

Sticky

top pages

27%

C1)

20%

)

100% C3

)

Page A

0.53 0.47

10050 49 1

0.4

0.58

few

25% C1)

35% C2)

17% C3)

23% C4)

WEB ANALYTICS - Analysing, Classifying and Describing Web Metrics with Fuzzy Logic

285

Figure 4: Example of fuzzily classified web metrics.

3.2 Comparison of Sharp and Fuzzy

In what follows a specific example of fuzzily classi-

fied metrics is presented, focusing on class 1 (C1 in

Figure 3 or 4). In a sharp classification, metrics of

pages belong strictly to one class only (e.g. to C1;

note that the data in Figure 4 is normalized in values

between 0 and 1).

In a fuzzy classification, we can select metrics

which belong to C1 with a certain degree of mem-

bership (for C1 in Figure 4a it is 33%). Figure 4b

shows that in comparison to a sharp classification:

Some data in the corner of the class 1 is no longer

considered as in that class when we use a fuzzy clas-

sification. That is, this data is undervalued in a sharp

classification. In contrast, other fuzzily classified

data do belong partially to C1 (Figure 4c), while in a

sharp classification they are excluded.

This comparison shows the differences between

a sharp and fuzzy classification of metrics. While in

a sharp classification web data belongs strictly to

one class, in a fuzzy classification data can be classi-

fied more appropriately and handled more flexibly.

In a fuzzy classification, the risks of misclassifica-

tion (under- or overvaluations) of data near the class

border are reduced.

3.3 Fuzzy Web Metrics Index

Classifications are not bound to one dimension (in

Figure 2) or two dimensions (in Figure 3 and 4). In

an index, for instance, any number of web metrics

(dimensions) can be modelled simultaneously.

If an index (I) is the aggregation of a number of

weighted (w) web metrics (wm) values of a website

with a number of different web pages (g) in period

(p), equation (1) in section 3.1 is adapted as follows

(4):

= w

()

(

)

(

)

∑

(4)

Eight metrics of table 1 are considered in the index

(5): page views (PV), visitors (VS), visits (VI), aver-

age time on page (TP), length of visit (LV), depth of

visit (DV), visit frequency (VF) and stickiness (ST).

= w

(

)

(

)

+ w

()

(

)

()

+ w

()

+ w

()

+ w

()

+ w

()

(

)

(5)

Assuming that all metrics have the same weight (w

=1), equation (4) can be defined, in which the value

) of a single web metric (wm) is assigned to a

linguistic term (class) “low”, “medium” or “high”:

low

()

(

)

medium

()

high

()

⎧

⎨

⎪

⎩

⎪

⎫

⎬

⎪

⎭

⎪

(6)

In the example of equation 7, a page 1 (g = 1) has

the following normalized values in period 1 (p = 1):

PV, 0.752

(

)

, VS, 0.389

(

)

, VI, 0.324

()

TP, 0.141

()

, LV, 0.108

()

, DV, 0.907

()

FR, 0.789

()

, ST, 0.945

()

⎧

⎨

⎪

⎩

⎪

⎫

⎬

⎪

⎭

⎪

(7)

In a sharp multidimensional classification (in Figure

5a), values between 0 and 0.333 belong to “low”,

0.334 to 0.666 to “medium” and 0.667 to 1 to

“high”. For instance, the metric visits (VI) with the

value 0.324 belongs to one class (“low” in 8) only:

VIsharp

= x

()

(

)

{

}

= x

low

()

{

}

(8)

Applying (2), in a fuzzy classification (Figure 5b)

the value of visits belongs to two classes in (9).

Fuzzily classified web

metrics not

belonging

to C1 (sharply classified

belonging to C1;

=> under

valued)

Fuzzily classified web

metrics partly

belonging

to C1 (sharply classified

not belonging to C1;

=> ove

valued

)

C1)

a) Fuzzily classified

web metrics belonging

with>33% to class C1)

b+c) Fuzzily classified web metrics partly

belonging to more than one class at the same time

WEBIST 2010 - 6th International Conference on Web Information Systems and Technologies

286

VIfuzzy

= x

()

{}

= x

low

,0.57

()

, x

medium

,0.43

()

{

}

(9)

Figure 5: Multi-dimensional sharp (a) and fuzzy (b) classi-

fication of an index with eight web metrics.

A fuzzy index, represented in graphic form in

Figure 5b, is an intelligent web analytics system

which can model and measure a website’s most im-

portant KPIs and web metrics. On an aggregated

level, it makes it possible to display and analyze the

performance of web usage and e-business, and to

compare different web pages and users (segments).

Moreover, it monitors changing values over time.

As the following sections will show, with a

fuzzy index both quantitative data and qualitative

criteria can be modelled with linguistic words.

Peterson proposes a sharp index to measure user

interactivity and engagement (Peterson 2006):

Engagement

= C

+ R

+ D

+ L

+ B

+ F

+ I

+ S

()

(10)

stands for intensive visits, R

for visit recency, D

for engaged visits, B

for brand index, F

for feed-

back index, L

for loyalty and S

for subscription

index. If users are more interactive and engaged, one

or more metric(s) increase and so does the index.

However, index values are abstract and difficult

to interpret. For this reason, WA needs a language-

based approach to reading and analyzing web data.

3.4 Web Analytics with Words

Sections 3.1 and 3.2 explained that linguistic terms

are used to describe membership functions of fuzzy

sets in order to classify metrics more exactly.

Moreover, the fuzzy logic approach makes it

possible to describe, analyze and evaluate results

and changes in web analytics using human language.

In soft computing, this is called computing with

words (Zadeh 1996, 1999) and the consideration of

perception-based information (Zadeh 2004).

For instance, a tool reported 1,718 page views in

June (see left of Figure 6). What does this measure-

ment-based information say to the analyst? Nothing,

as long as the analyst cannot compare this absolute

number with an internal or external benchmark. If in

July 5,897 page views were recorded, the analyst

knows from experience that this is ”much more”

than the month before. If the number of visitors in-

creased by 2.3% in 2009, the analyst might state that

the number of visitors ”increased slightly” in 2009.

Many other examples of analysis show that humans

have a perception-based rather than a measurement-

based approach to interpreting, describing and com-

municating web data and information.

WA tools report oodles of numbers, but web ana-

lysts often have difficulty interpreting web data,

recognizing trends and deriving useful conclusions.

In future intelligent web analytics systems will ana-

lyze and interpret data in a (semi or fully) automatic

system. They will give meaningful answers to rele-

vant business questions, which can be selected or

formulated in natural language. For instance, an ana-

lyst may ask a fuzzy web analytics system:

• Which web or product pages have “many pages

views” and “low bounce rates” (C1 in Figure 3)?

• Which product pages have “high order rates”?

• Which products or buyers have “high revenues”?

• Which visitors are “very loyal” and have “high

engagement”?

3.5 Definition of Fuzzy Concepts

The fuzzy logic approach makes it possible to work

with quantitative metrics (hard facts like revenues)

and qualitative variables (soft criteria like engage-

ment or loyalty) at the same time.

In fact, the strength of the fuzzy logic approach

is the possibility to define and use qualitative, lin-

Page views

Visitors

Visits

Time on page

Length of visit

Depth of visit

Visit

frequency

(loyalty)

Stickiness

Page views

Visitors

Visits

Time on page

Length of visit

Depth of visit

Visit

frequency

(loyalty)

Stickiness

low

edium

e 1

0.333

0.666

0.389: 100%

medium

0.324: 100%

low

58% mediu

42% low

58% mediu

43% mediu

57% low

VSsharp

= x

medium

(

)

{

}

VIsharp

= x

low

(

)

{

}

VSfuzzy

low

,0.42

()

medium

,0.58

()

⎧

⎨

⎪

⎩

⎪

⎫

⎬

⎪

⎭

⎪

VIfuzzy

low

,0.57

()

medium

,0.43

()

⎧

⎨

⎪

⎩

⎪

⎫

⎬

⎪

⎭

⎪

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287

guistic variables besides quantitative ones, which is

not possible in binary computing.

Figure 6: Measurement-based and perception-based

information about website traffic.

For example, a analyst may define the following

fuzzy concepts (Fasel & Zumstein 2009):

• “high traffic period”,

• “above-average conversion rates”,

• “strong online customer loyalty”,

• “attractive web pages” or

• “high visitor value”.

Time is an example of a dimension which benefits

significantly from the use of fuzzy constructs. It does

not suddenly become evening at 6 pm, or night at 10

pm. Human beings perceive a fluent transition be-

tween afternoon, evening and night (see Figure 7a).

Similarly, different seasons such as spring, summer,

autumn and winter do not start and end abruptly, and

neither do seasonal variations, like the high season

in summer (in Figure 7b).

In a sharp classification of the construct ‘eve-

ning’, only page views between 6 pm and 8 pm are

displayed (but arbitrarily not those at 5.59 or 8.01).

Within a fuzzy logic approach, page views after 4

and up until 10 pm are considered to have a certain

membership degree (at 5 pm this is 50% afternoon

and 50% evening in Figure 7a). Moreover, uncer-

tainty and imprecision can be taken into account. For

instance, warm summertime is “most” between June

and August, but “sometimes”, summers already start

in May and end later in September (Figure 7b).

Fuzzy time concepts are promising for web ana-

lytics, since they allow new types of deeper analysis.

For example, the web analyst can query: “give me…

• all web pages with many page views and low

bounces rates in the evening.”

• all web pages with high conversion rates in the

high season.”

• the most loyal customers with high purchase fre-

quency and high online revenues.”

The more metrics are considered in web analytics,

the more difficult it is to draw conclusions from the

web data. Qualitative statements in human language,

which are transformed into computer language by

fuzzy logic, reduce complexity and help to analyze

and interpret web usage data.

Figure 7: Fuzzy time constructs: afternoon, evening and

night (a), and summer (b).

4 WEB CONTROLLING LOOP

To implement fuzzy web analytics effectively, man-

agement first has to define the goals of a web offer

at a strategic level (number 1 in the WA architecture

of Figure 8). Websites have many objectives, such

as informing, communicating, branding, advertising,

lead generating, selling, supporting, entertaining or

community-building. Consequently, website success

is linked to the achievement of specific goals

(Bélanger et al. 2006). KPIs and metrics are derived

and defined according to the goals of the website

(2). After collecting data on the website and data

layer, metrics are analyzed and controlled regularly

in an ongoing process of web controlling (4 to 5).

The web controlling loop (6) makes it possible to

monitor the achievement of website objectives and

plans (3), and to (re)act on an operational level (7).

Finally, the controlling loop enables the ongoing

optimization of website quality, electronic marketing

and CRM in a dedicated manner. This permits web

managers to allocate resources more effectively.

Time (hours pm)

12 1 2 3 4 5 6 7 8 9

Afternoon

Evening Night

Time (month)

May June

July

August

September

Summer (high season)

sometimes

most

0.5

measurement-based

numerical

perception-based

linguistic

Web analytics information

(web data and metrics)

 Total page views:

June 2009: 1,718

July 2009: 5,897

 Number of visitors

2009: +2.3%

 Most visits lasted

0-10 seconds

 The site had many more

page views in June 2009

than in July 2009

 The number of visitors

increased slightly in

2009

 Most visits lasted:

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Figure 8: Web analytics architecture with different layers

and the web controlling loop.

5 CONCLUSIONS

This paper has introduced a fuzzy logic approach to

web analytics and discussed a number of indicators

and metrics in web analytics.

Fuzzy web analytics has several advantages:

• precise classification of elements (e.g. web data

and metrics) in classes and a gradual ranking

within classes

• reduction of complexity (of web data and infor-

mation overload) without loss of information

• use of quantitative variables (numerical values)

and qualitative variables (non-numerical values)

• use of linguistic variables or terms for queries

and computing with words

• human-oriented, perception-based and intuitive

processing of web data, metrics and information

• dynamic and multidimensional analysis consider-

ing different metrics, and the

• consideration and mapping of concepts and con-

structs which are intrinsically fuzzy, i.e. vague,

uncertain or subjective per definition.

Nevertheless, fuzzy logic is confronted with certain

problems:

• Sharp classification is usually clear, simple and

straightforward for everyone. In contrast, fuzzy

classification is more complicated, not as easy to

understand, to communicate and to implement.

• Fuzzy classifications can be confusing or even

contradictory, if an object can belong to differ-

ent, conflicting classes at the same time.

• In practice, some decisions have to be "sharp". In

these situations, fuzzy classifications may not be

adequate.

Moreover, the theoretical approach of this paper has

to be tested with real web data from e-business in

future studies. In fact, case studies with firms are

already planned to show the advantages and limita-

tions of the fuzzy logic method in web analytics.

The research center Fuzzy Marketing Methods

(www.FMsquare.org) is engaged with applications

of fuzziness to different domains in information sys-

tems. FMsquare provides a number of open source

prototypes, including the fCQL (fuzzy classification

query language) toolkit, which enables fuzzy queries

and the calculation of membership degrees of data

stored in MySQL or in PostgreSQL databases.

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