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ANDREA RANGONE AND RAFFAELLO BALOCCO
INTRODUCTION
Business-to-consumer (BtoC) e-commerce has increased during the last
years as the Internet has grown and
spread almost world-wide. The literature on decision-making variables at a
company’s disposal to improve its ecommerce strategy is contrasting,
probably because of the evolutionary
nature of the matter. On the one
hand, there is increasing interest in
in-depth study of specific topics, such
as online marketing, website design
and implementation, and transactions
security over the Internet, while, on
the other, some very important subjects, e.g. logistic process management, the means to determine
product portfolio choices and pricing
strategies with regards to e-commerce
have received little attention. Besides
these varying levels of interest in different otpics, little attempt has been
made to define a systemic framework
integrating decision-making variables
and performance indicators that can
be used by management in planning
and controlling e-commerce strategy.
The present paper aims to provide a
systemic framework of the decisionmaking variables available to a company running a BtoC e-commerce
business and of the performance indicators for an ex ante evaluation and
an ex post control of actions on these
variables. This paper deals with the
following topics:
1. Contributions in the literature;
summary of contributions from
academics, practitioners and consultants concerning decision-making variables on which an ecommerce company can act, and
performance indicators to control
the actions on those variables;
2. Decision-making variables; the ecommerce mix framework provides
a systemic view of the decisionmaking variables that management
can use in the planning and decision-making process;
3. Performance indicators; the framework defines a set of indicators to
evaluate and monitor the effects of
action on e-commerce mix variables.
A
b
s
t
r
a
c
t
The literature on decision-making variables at a company’s disposal to improve its e-commerce strategey is
contrasting, probably because of the
evolutionary nature of the matter. Besides the varying levels of interest in
different topics, little attempt has been
made to define a systemic framework
integrating decision-making variables
and performance indicators that can be
used by management in planning and
controlling a BtoC e-commerce strategy.
The present paper aims to provide a
systemic and overall framework both of
the decision-making variables available
to a company running a BtoC e-commerce business and of the indicators
for an ex ante evaluation and ex post
control of actions on these variables.
CONTRIBUTIONS IN THE
LITERATURE
The analysis of the literature dealing
with decision-making variables relevant to an e-commerce business covers three main areas, website design,
security over the Internet and payment methods, and online promotion, and a number of residual
matters, such a logistic process, pricing strategy and pre- and after-sales
services.
As regards website design, there is
general agreement on the different
issues such as Web design, Web navigaton and Web usability (e.g. Bucha-
A
u
t
h
o
r
s
Andrea Rangone
(andrea.rangone@polimi.it) is Professor
of e-commerce at Politecnico di Milano.
His research focuses on performance
measurement systems for e-commerce
and strategic analysis and economic
justification of e-business projects.
Raffaello Balocco
(r.balocco@libero.it) is a research fellow
at Department of Economics and
Production at Politecnico di Milano.
Copyright © 2000 Electronic Markets
Volume 10 (2): 130–143. www.electronicmarkets.org
A Performance Measurement
System for Planning and
Controlling a BtoC E-Commerce
Strategy
Keywords: e-commerce, decision making, performace evaluation
RESEARCH
Electronic Markets Vol. 10 No 2
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nam and Lukaszewski 1997; Instone 1997; Nemzow
1997; Ruh et al. 1997; Fleming 1998). Neverthless, one
problem is still open: the application of principles and
general guidelines to e-commerce websites. Although a
number of authors have studied and described in depth
the principles for designing and implementing generic
websites (e.g. Emery 1997; Graham 1997; Heba 1997;
Helinski 1997; Nemzow 1997; Parker 1997; Radosevich
1997; Scholtz 1997; Siegel 1997; Sullivan 1997; Whinston et al. 1997; Fleming 1998; Haine 1998; Morville
and Rosenfeld 1998; Lynch and Horton 1999; Wilson
1998, Tilson et al. 1998) some gaps remain in the
application of these principles to e-commerce websites
and, therefore, to the process of ordering products and
commercial content.
Internet security is addressed in a good number of
contributions focusing attention on cryptography systems
(public and private key cryptography) that ensure secure
data transmission, and on-line payment methods based on
the Secure Socket Layer (SSL), Secure Electronic transaction
(SET) or virtual cash systems, such as Mondex or Digicash
(e.g. Bhimani 1996; Panurach 1996; Spar and Bussgang
1996; Kalakota and Whinston 1997; Nemzow 1997;
Borenstein 1998; Honeycutt et al. 1998; Treese and
Stewart 1998).
In the studies on online promotion, the emphasis given
by many authors (e.g. Hoffman and Novak 1996; Alba et
al. 1997; Hamil 1997; Kannan et al. 1998; McCullough
1998; Nadherny 1998) to the new communication channels made available over the Internet is not reflected in
equal attention to the ‘operational’ tools at a company’s
disposal. While various authors have developed theories
regarding new communication models, few have sought to
define a set of guidelines to assist management in promoting and advertising the e-commerce website.
The literature also reveals that some subjects concerning
e-commerce decision-making variables have still received
little attention. These include:
131
· the logistic process, which is critical in BtoC e-commerce because of the element of home-delivery;
· the portfolio choices, which are of critical importance,
since each product type has a different degree of saleability over the Internet;
· pricing strategy, which might have specific features in ecommerce;
· pre- and after-sales services, such as the virtual shop
assistant or the order tracking and interactive services,
which aim to increase the value of the commercial
activity and may represent an effective variable for online
differentiation.
The work on the performance indicators available to
companies to control and monitor e-commerce performance fails to provide an overall view of all the decisionmaking variables. In particular, the authors focus on
monitoring promotion and the level of usability of the site.
The indicators for the former are essentially based on the
analysis of log-files (e.g. Buchanam and Lukaszewski
1997; Emery 1997; Treese and Stewart 1998), while the
techniques to test site usability are termed Web Usability
Engineering (Buchanam and Lukaszewski 1997; Instone
1997; Nielsen 1998) and can be divided into two categories:
· user testing: site users are asked to complete questionnaires from which the usability level can be calculated;
· heuristic evaluation: tests undertaken by a small group
of experts to verify violations of fundamental principles
for navigation, design, speed and compatibility.
Overall, the analysis of the literature highlights the lack
of a framework that integrates all decision-making variables
relevant to a BtoC e-commerce project, and of a performance measurement system able to measure the effects of
management’s action on those decision-making variables.
The e-commerce mix framework described in the following
chapters provides a systemic view of these decision-making
variables and performance indicators. Specifically, the framework has two objecties:
1. to support management in the planning and decisionmaking process;
2. to control the effects of action on decision-making
variables, so initiating a learning process that might be
critical in future decisions.
THE DECISION-MAKING VARIABLES
The variables on which the company can act to increase
Net Cash Flow are divided into three categories on the
analogy of the concept of ‘marketing mix’ (Kotler 1997),1
since these variables strongly affect the attractiveness of
online business and, therefore, the expected revenue (see
Figure 1):
1. website variables;
2. products and services variables;
3. promotion variables.
These macro-categories are then broken down hierarchically to give a group of elementary variables.
Website
This macro-variable is broken down into three first-level
variables: web interface, transaction management and content.
Web interface. The interface includes all the web pages
and their links in the virtual store. As shown in Figure 1,
the web interface can be taken as the result of three
variables:
1. web design, for which the elementary variables: (a) site
architecture, i.e. the conceptual design of the links
Transactions. Online transactions involve two phases:
1. order process, i.e. how the customer chooses the products to buy and sends the order together with the
relevant personal data. This variable is divided into two
basic variables: (a) the order system, i.e. shopping cart,
online form, e-mail; (b) the order process, i.e. the
navigation path followed by the user from the initial
product selection to completion of the order placement, that may merely consist of a single click (see, for
instance, Amazon one-click-ordering service);
2. payment, which may be online by credit card or virtual
cash, or offline by bank transfer, cheque, money order
or cash-on-delivery.
Content. The informational content within a website can
be classified in two categories:
1. commercial information, including a full description of
the product range (images and text), the means of
delivery (options, times, costs) and the method of
payment (description of the security system used for
data transfer);
2. background content, such as ‘about us’ information,
details on the economic sector or other information
not strictly related to the business activity (see, for
instance, Peck.it ‘about us’ web page, where customers
can find the story of the store and other related
information).
Products and Services
This macro-variable is broken down into five first-level
variables: products, pre- and after-sales services, pricing
strategy, delivery services and virtual community services.
Planning and Controlling a BtoC E-Commerce Strategy
between web pages and, therefore, the organization of
content; (b) graphics, i.e. the use of images, text and
frames, and the layout of these elements in the web
pages;
2. navigation system, divided into: (a) navigation through
pages, i.e. the use of tools (links navigation bar etc.) to
move from one page to another; it is possible to find
many examples of the use of such tools browsing the
best known web stores: Amazon.com, Cdnow.com,
BarnesandNoble.com, Landsend.com etc.; (b) navigation in the same page, i.e. a design which allows easy
navigation within a single page; (c) information search,
i.e. the use of tools (search engine, site map, table of
contents) to find information or products; those tools
are useful to improve web store navigability as the
depth and the width of the products range increases:
Peck.it, a web store of Italian gourmet, that offers
thousand of wines from serveral Italian producers,
allows customers to search by name, producer, year,
region, type and price;
3. personalization, i.e. the possibility for users to personalize web pages (e.g. the home page elements) according
to their needs and tastes; Dell.com, for instance, allows
customers to personalize the support of section of the
web store (http://support.dell.com) according to their
technical skills and computer features.
Rangone
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Figure 1. E-commerce mix decision-making variables
132
Products. The choice of products to offer via the website
can be based on the following variables:
1. portfolio, i.e. the type of the products and the breadth,
depth and complementarity of the range;
2. product differentials, i.e. the products competitive
advantages with respect to online and offline competitors (e.g. quality, brands, etc.).
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Pre- and after-sales services. The Internet is an extremely effective and efficient means of offering customers
pre- and after-sales services. It is effective because it
supports a range of high value added services to the
customer, and efficient because the company can reduce
the costs traditionally associated with these services. This
lever can therefore be used both to differentiate with
respect to competitors and to create cost differentials in
particular with offline suppliers.
Over the Internet it’s easy to find pre- and after-sales
services. Here there are some examples: Landsend.com
helps the customers build a 3-D model using exact measurement for virtually trying clothes on before buying (see
‘Your personal Model’ web page); Garden.com allows web
navigators to design a virtual garden using the ‘Garden
Planner’ service and to order all the selected plants and
flowers by a single-click; CdNow.com allows customer to
listen to some tracks from the CDs sold over the
web store; several computer web store (e.g. Dell.com,
IBM.com, Hp.com, etc.) offer an online trouble-shooting
and customer support services.
Electronic Markets Vol. 10 No 2
Pricing strategy. The elements to be considered in the
definition of a pricing strategy are:
133
1. price level, in comparison to online and offline competitors. In electronic commerce, companies can use price
discrimination and through market segmentation establish different price levels for different groups of customers. Using online tools such as active agents (e.g.
MySimon.com), it is also possible to continually monitor prices set by online competitors in order to avoid
different price levels with respect to them;
2. discount policies, allow the company to increase the
number of purchases via the Web. The company can
provide different type of discounts (e.g loyalty discounts, cross selling discount, etc.).
Delivery services. This is a particularly critical variable for
BtoC e-commerce, because a sales channel via the World
Wide Web is accessible from everywhere (unless the
company introduces its own restrictions), meaning that
there is no spatial relationship between the customer and
the supplier: an order can arrive from any Internet user.
The company or the supplier must be able to deliver
quickly for a reasonable cost. The decision-making variables affecting delivery on which the company can act are:
1. front-office, i.e. the delivery methods offered and the
tracking system, which determine how the customer
perceives the service;
2. back-office, i.e. distribution channel (internal or external, existing or new), inventory management (which
products are held in stock and where) and the logistic
process.
Virtual community services. The company can stimulate
the development of a virtual community on its website for
a number of reasons: to collect information on the
members of the community, to establish long-term relationships with users to create loyalty, to focus efforts on a
group of consumers with similar interest. The typical
services of a virtual community can be divided into two
categories:
1. interactive services, divided into among users and userwebmaster services. The former can be direct when
information is transmitted without any involvement of
the company (e.g. non-moderated chat and discussion
forums), or indirect when the company verifies content
before publication (FAQ, user feedback). The userwebmaster services services primarily facilitate contact
between potential customers and the company (chat
with company employees, newsletter). Garden.com, for
instance, organises virtual meetings where customers
can chat online with expert gardeners and doctors;
2. informational content strictly connected to the social
dimension; i.e. information services which develop a
sense of community within the website. For example,
Studiomoda.com, a web store selling Italian apparel
and accessories, contains an electronic magazine called
‘YesPlease’ about Italian style.
Promotion
This macro-variable concerns all the decision-making variables on which it is possible to act to increase Web store
visibility. Two first-level variables can be defined: online
promotional channels and offline promotional channels
based on traditional methods of advertizing.
Online promotiuonal channels. These can be divided into
two categories:
1. based on the WWW, which includes search engines, web
directories, banners, insterstitials, the exchange of links
and affiliate programmes;
2. below the WWW, i.e. advertizing postings, sponsorships,
direct e-mailing lists, online public relations.
THE PERFORMANCE INDICATORS
This chapter proposes a system of indicators, which
company management, can use to:
· make the most effective choices when planning a BtoC
e-commerce strategy;
· monitor action on decision-making variables constantly
and, in the benchmarking phase, compare the performance of its own Web store with that of competitors;
· assess the performance of its own strategy to ensure
improvement and learning processes.
The starting point in the definition of the indicators is the
objective that a company involved in e-commerce sets.
Strategic literature states that a long-term objective for a
company must be the creation of economic value, defined
as the sum of NCFs over a given time horizon which goes
from 0 to infinity:
First level; the incoming cash flows are given by the
product of the number of buyers (n. buyers) and the
average value of a purchase (Ap). The two values are
calculated separately for first-time (1st time) and returning
(returning) customers.
Second level; the number of new customers (n. buyers
1st time) depends no the number of visitors accessing the
site for the first time, the percentage of visitors exiting the
site after having viewed the access page and thus not
entering the site, (exit rate), and the percentage of visitors
making a purchase (conversion rate). For the customers
who have already made a purchase, the relationship is
similar, except that the exit rate is not significant: a
customer returning to the site is obviously interested in its
content.
Third level; The number of first-time visitors is calculated as the sum of the visitors learning of the site through
the company’s promotional activities,
and those discovering
the site through word-of-mouth. The number of returning
customers can be divided into those returning as a result
of promotional activities, those returning spontaneously,
i.e. without being induced by a promotional activity, and
customers encouraged by the company to return through
e-mailings, i.e. mailings or newsletters publicising promotions, new products, new sections in the site, etc.
Figure 2 also shows the relationships between the basic
components of the cash flows and the three macro
variables of the e-commerce mix framework: promotion,
website and products and services.
1
X
NCF(t)
(1 ‡ r) t
If the opportunity cost of capital (r), which depends on
the level of risk involved in the commercial activity, is
taken as fixed, economic value is given by Net Cash Flow,
that can be seen as the difference between cash inflows
and cash outflows:
NCF ˆ FFIN - FFOUT
Below, two groups of indicators (financial and nonfinancial) for incoming and outgoing cash flows are
proposed, highlighting how they are measured and their
limitations.
Specifically, promotion influences:
Indicators for Cash Inflows
Figure 2 shows the breakdown of cash inflows into the
basic components which are affected at different levels by
the three macro variables identified in the e-commerce mix
model that we call value driver products and services, web
site and promotion).
The following sections describe the breakdown of
incoming cash flows illustrated in Figure 2.
·
number of visitors (for both first and return visits);
exit rate, as the promotional message becomes more
focussed on the right company customers percentage of
users leaving the site after viewing the access page
diminishes they are more interested in the products
offered;
· conversion rate; there is an indirect correlation between
promotion and conversion rate: as the level of focus on
·
Planning and Controlling a BtoC E-Commerce Strategy
t ˆ0
Rangone
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V (0) ˆ
Figure 2. Breakdown of cash inflows
134
the right company customers increases, the probability
that users make a purchase on the site is greater;
· number of visitors returning by e-mailing; through
promotions based on e-mailing customers and registered
users (newsletter, direct mailing, mailing list), the company can encourage customers to return to the site.
The website influences:
·
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number of visitors returning spontaneously or through
word-of-mouth. The overall effectiveness of a web site
(interface, order and payment system, content) can
encourage customers to return spontaneously to the site
and may favour word-of-mouth;
· conversion rate; the usability of the web interface (clear
navigation, good design, compatibility and speed), the
effectiveness of the informational content and the transaction system (order and payment) increase the probability that users make purchases;
· exit rate; the effectiveness of the home or site access
page has a major impact on the percentage of users
leaving the site after seeing this page.
Products and services influence:
·
number of visitors returning spontaneously or thorugh
word-of-mouth. The features of the products and services portfolio can encourage customers to return spontaneously to the site and may favour word-of-mouth
publicity;
· conversion rate, as for the web site, a good products and
services portfolio influences the number of users placing
an order.
For each of the three categories (website, products and
services and promotion), a number of indicators have been
defined, underlining their limitations and the difficulties in
measurement when evaluating and monitoring the effects
of acting on e-commerce mix decision-making variables.
Performance Indicators for the Website
Electronic Markets Vol. 10 No 2
To assess the effectiveness of the website, three methods
are proposed which can be classified in two dimensions:
135
· output of the assessment (analytical or synthetic): the
analytical output allows individual features of the site to
be monitored, while the synthetic output gives an overall evaluation of the site effectiveness;
· the subject performing the assessment (internal to the
company or visitor/user).
Figure 3 classifies the the three methods proposed in
two dimensions:
As shown in Figure 3, a synthetic method applied by an
external subject (lower right cell) is not very significant.
The output is an overall judgement of the effectiveness of
the site which, as evidenced below, can be deduced by reprocessing an analytical survey (upper right cell).
Figure 3. Methods to assess web site effectiveness
Fuzzy Model
In order to obtain an overall, synthetic assessment of the
effectiveness of an e-commerce site a model based on
Fuzzy Set Theory (Zadeh 1965; Lee 1990; Zimmermann
1991) is proposed. Figure 4 shows a group of 35
indicators whose values can be used to calculate overall
effectiveness. These indicators can be divided into two
categories:
1. boolean indicators which can be measured by On-Off
type values. This category includes all the indicators in
round brackets in Figure 4 (e.g. presence of a navigation bar or no ambiguous links or dead-end documents
as navigability);
2. discrete indicators, which can be measured qualitatively
(e.g. High-Medium-Low). Examples of such indicators
(in square brackets in Figure 4) are attractiveness and
descriptive power for the assessment of the effectiveness
of the images.
The following paragraphs describe an assessment model
based on Fuzzy Set Theory which is able to convert all types
of boolean indicator intod iscrete indicators and subsequently integrate all these indicators to obain an assessment of the overall effectiveness of a virtual store or one of
its components. Specifically, two models based on Fuzzy
Set Theory 2 are used:
1. the fuzzy heuristic model to transform the values of the
boolean indicators into values of a linguistic variable
(Dubois and Preade, 1984; Lee 1990; Kosko 1993);
2. the fuzzy linguistic model to provide an overall assessment of the effectiveness of the website based on the
effectiveness of its components (Zimmermann 1991;
Rangone 1997; Perego and Rangone 1998).
Implementaton of the fuzzy model requires the following steps:
1. Determine the importance weightings of the various
discrete indicators identified in the analytical model.
2. Define the decision-making rules of the fuzzy heuristic
model for the boolean indicators and assess the performance of the virtual store for each indicator;
3. Calculate the overall effectiveness of the virtual store by
weighting the assessments with the importance weightings.
Step 1. Determine the importance weighting of the
discrete indicators. In a fuzzy model, the relative impor-
S X ˆ (Very High, High, Medium, Low, Very Low),
where (Lee 1990; Liang and Wang 1993; Perego and
Rangone 1998): Very Low ˆ (0;0;0,3), Low ˆ (0;0,3;
0,5), Medium ˆ (0,2;0,5;0,8), High ˆ (0,5;0,7;1), Very
High ˆ (0,7;1;1).
Step 2. Define the decision-making rules to apply to the
fuzzy heuristic model for the boolean criteria and assess
the performance of the virtual store for each assessment
criterion. Definition of the decision-making rules allows
all the possible combinations of values for the boolean
criteria to be converted into the fuzzy linguistic values
used to assess the virtual store.3 For example, the following decision-making rules may be used to asses graphical
coherence.
Graphical coherence [GC]
Criterion
Subcriteria
GC
GC1
GC2
GC3
GC4
Description
Overall
coherence graphical
Consistent background
for each section of the
site
Consistent navigation bar
Consistent fonts
Consistent layout of the
various pages
Values
On/Off
On/Off
On/Off
On/Off
1. If [(GCi ˆ On)], then [GC ˆ Very good], 8 i ˆ
1, . . . 4. If all criteria are On, the graphical coherence is
Very good.
2. If [(GCi ˆ Off )] then [GC ˆ Good], i ˆ 2, 3, 4. If
only one criterion is Off, except background (i ˆ 1),
graphical coherence is Good.
3. If [(GC1 ˆ On) And (GCi ˆ GCj ˆ Off )] then
[GC ˆ Fair], i 6ˆ j ˆ 2, 3, 4. If the background is uniform, but two of the other three criteria are Off, the
coherence is Fair.
4. If [(GC1 ˆ Off ) then [GC ˆ Poor]. If the background
is not consistent in each section, graphical coherence is
Poor.
5. If [(GCi ˆ Off ), then [GC ˆ Very poor], 8 i ˆ 1,
. . . 4. If all the criteria are Off, graphical coherence is
Very poor.
Now it is necessary to measure the performance of the
virtual store on each indicator:
1. For the discrete indicators, the linguistic variable W is
used, as in the definition of the decision-making rules
in Step 2. These can be measured on the following
linguistic scale (Liang and Wang 1993; Perego and
Rangone 1998): S W ˆ VP, P, F, G, VG, where
VP ˆ Very Poor (0, 0, 0.2), P ˆ Poor (0, 0.2, 0.4),
F ˆ Fair (0.3, 0.5, 0.7), G ˆ Good (0.6, 0.8, 1),
VG ˆ Very Good (0:8, 1, 1);
2. For the boolean indicators, the decision-making rules
devised in Step 2 must be applied, in order to obtain
a linguistic value belonging to the linguistic variable
W .
Planning and Controlling a BtoC E-Commerce Strategy
tance of the different assessment criteria is measured by
means of a linguistic variable, using the following scale:
Rangone
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Figure 4. Framework to evaluate website effectiveness
136
Step 3. Calculate the overall effectiveness of the virtual
store. To calculate the overall effectiveness, the formula
proposed by Zadeh can be used (Zadeh 1965):
K
ˆ (1=m)
[(A 1
(A 3
W3)
W 1)
K
(A 2
(A m
Score
1. Graphical coherence in the various
sections of the site
2. Legibility of the web pages
3. Quality of the images
4. Navigation between pages
5. Locating information/products within
the site
6. Download speed of the web pages
7. Ordering (system and process)
8. Payment and security system
9. Textual description of the products
10. Product images
11. Background content (about us, information
about the sector, other information)
From 1 to 5
W2)
Please assess:
W m )]
where:
is a fuzzy number representing the overall effectiveness of the virtual store;
W J is the weighting of the jth assessment criterion;
A j is the linguistic value for the jth criterion and m the
number of assessment criteria.
K
From
From
From
From
1
1
1
1
to
to
to
to
5
5
5
5
From
From
From
From
From
From
1
1
1
1
1
1
to
to
to
to
to
to
5
5
5
5
5
5
Set of Indicators
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An easier way to mmonitor site performance that can be
used if there’s no need to have a synthetic output but it is
sufficient to monitor some critical performance, is to
define a set of indicators starting from those given in the
fuzzy model (see Figure 3). It is prefereable to consider
only the quantitative indicators, which can be calculated
objectively, while the qualitative values should be obtained
using the analytical survey detailed in the next section.
Example indicators might be:
· products;
· pre- and after-sales services;
· pricing strategy;
· delivery;
· virtual community services;
· download speed of the web pages;
· number of broken links;
· number of dead-end pages;
· average percentage of page errors during a session.
The company must decide the most appropriate set of
indicators, basing this choice strategy to be adopted, on its
sector, on the external context in which it operates, etc.
Products
Some possible indicators that can be used to monitor the
effectiveness of an e-commerce product portfolio are:
4
these indicators can be measured with special software
and allow the company to monitor the quantitative variables which affect the site usability.
Electronic Markets Vol. 10 No 2
Analytical Survey
137
This involves asking users to assess the effectiveness of
different features of the site. The starting point for the
questionnaire can be the indicator tree used in the fuzzy
model (see Figure 4). The company can decide how far to
go down the tree and which parts to emphasise. The
visitors are asked to respond to a questionnaire, which is
sent by e-mail or available as an online form. Responses
can be evaluated using, for example, a points score
method. An example questionnaire is given below.
The score for each response can be summed (e.g. as a
weighted average) to obtain o aggregated assessments or
even a full overall evaluation.
·
range, i.e. the number of different product types (range
width) and variants of each type (range depth) in comparison with online and offline competitors. This can be
measured directly within competitors’ sites for online
competitors or by an analysis of each traditional sales
channel for offline competitors, as the same product
type can be offered in different ranges in function of the
sales channel;
· product competitive differntials, which measures the
presence of attractiveness differentials linked to product
features in comparison with online and offline competitors (e.g. quality, brands, etc.). As for the breadth and
depth of the product range, it can be measured directly
within competitors’ sites for online competitors and by
an analysis of each traditional sales channel for offline
competitors.
Pre- and After-sales Services
Performance Indicators for Product and Services
For the products and services variable, various indicators
are proposed which refer to:
There are many pre- and after-sales services and, as stated
previously, they often depend on the type of product
traded (see above). The indicators which can be used to
monitor the effectiveness of these services can be divided
Delivery
into two categories on the basis of the means of measurement:
Three possible indicators to measure delivery effectiveness
are proposed:
· indicators measured by the company itself using log
files;
· indicators measured by survey.
·
Some possible indicators which the company can measure
are:
Number of options offered. This measures the number of
delivery options offered by competitors on their sites in
order to conduct comparative analyses. The indicator
can be obtained by an online analysis of competitors’
sites in the same strategic group (for comparable products).
· Delivery time. It measures the delivery time applied by
competitors in order to conduct comparative analyses
and can be obtained by:
The indicators which can be measured by survey are
heavily dependent on the type of service provided by the
company. Generaly speaking, the company can measure:
1. an online analysis of competitors’ sites in order to
verify the declared data;
2. an empirical survey by means of an order placed with
a competitor;
3. a customer surveys to obtain own delivery time.
·
· the level of use of pre-sales services by visitors and
customers;
· the level of use of after-sales services by customers;
· the effectiveness of each service from the user’s point of
view;
· possible improvements to existing services;
· the need for new services.
Distribution of preferences between different delivery
options. It is useful to identify possible shortcomings in
the options offered and it can be obtained by analysis of
orders received over a given period of time.
Virtual Community Services
· Indicators measured by the company itself using log
files;
· Indicators measured by survey.
Pricing
·
The indicators used to monitor pricing strategy measure
the differentials with respect to online and offline competitors, as well as the handling and shipping charges applied
by other suppliers.
Price differentials with respect to offline competitors. The
indicator can be obtained by monitoring the prices of
competitors operating in traditional channels (for comparable products). It measures the customer advantage
in purchasing online rather than through traditional
channels;
· Price differentials with respect to online competitors. The
indicator can be obtained by an online analysis of
competitors’ sites in the same strategic group (for
comparable products). It measures the customer advantage in purchasing online from one company rather than
from another;
· Delivery charge. The indicator measures the delivery
charge applied by competitors in order to conduct
coparative analyses. The indicator can be obtained by an
online analysis of competitors’ sites.
A number of indicators which can be measured internally are described below:
·
particpants in virtual community, which is useful to
evaluate the level of interest in the virtual community;
· participants in an interactive service/website visitors,
which evaluates the level of interest in the interactive
service. This can be measured with log files only if the
interactive services (e.g. chat, discussion forum, mailing
list) require a specific user registration;
· buyers registered in the community/total buyers, indicates
the community impact on web store sales.
A survey can measure indicators similar to those described above for pre- and post-sales services (level of use,
effectiveness, possible improvements, need for new services), as well others linked to the social dimension. These
latter include, for example, relationships between users,
effectiveness of interaction with the webmaster, usefulness
of contributions received from users.
Planning and Controlling a BtoC E-Commerce Strategy
As for the pre- and post-sales support, the interactive
services of the virtual community can also be monitored
by two types of indicator:
Rangone
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· number of users accessing a given service/overall number
of users; this gives the level of interest in each service;
· percentage of customers which use a pre-sales service prior
to purchase; this measures the effectiveness of each presales service;
· percentage of customers which use an after-sales service;
this measures the usefulness of the service for the
customer, but can only be measured if registration is
requested to access the service.
138
Online Promotion Indicators
tors, some of which are measured differently in function of
the channel in question:
This section proposed a set of indicators to assess the
effectiveness of online promotion in increasing website
visibility.
The effectiveness of the ith promotional channel can be
calculated by the following equation:
i
Nbuyers
BDG i
·
(1)
CPM is useful to evaluate the cost of promotional
activity. The measurement depends on the promotional
channel in question;
· click-through rate evaluates the efficiency of a promotional channel and of the message content. The measurement depends on the promotional channel in
question;
· exit rate evaluates the efficiency of the message content.
It can be obtained by log file analysis;
· conversion rate also assesses the correct targeting of
promotional activity, and can be obtained by log file
analysis.
where:
i
N buyers
is the number of buyers who entered the site via
the ith promotional channel
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BDG i is the budget allocated for the ith promotional
channel.
Obviously, the assessment of the effectiveness of a given
promotional channel by means of the ratio the number of
buyers and the budget presupposes that the company’s
objective is the increase site sales. Other effects, which a
promotional campaign might provoke, e.g. increased
brand awareness, are not considered.
The equation (1) can be further broken down into some
basic components:
i
N buyers
BDG i
ˆ
i
N impression
BDG i
where:
i
N impression
is the number of times that the promotional
message is seen by potential customers through the ith
channel; CTR i (click through rate) is the percentage of
visitors entering the site after having seen the promotional
message through the ith channel; ER i (exit rate) is the
number of visitors arriving through the ith channel who
leave the site after seeing the access page; CR i (conversion
rate) is the percentage of visitors arriving through the ith
channel who make a purchase; CPM i (cost per million) is
the cost of 1000 impressions through the ith channel.
The effectiveness of each promotional channel can
therefore be monitored using the following four indicaElectronic Markets Vol. 10 No 2
· search engines and Web directories;
· banners;
· other tools based on e-mail (newsgroups, and mailing
lists, etc.);
In addition to the indicators already described, various
specific criteria for individual channels are also given.
. CTR i . ER i . CR i
CTR i . 1:000 .
ˆ
ER i . CR i
CPM i
139
In the next section, the means of measuring CTR and
CPM is given for the following promotional channels:
Search engines and Web directories. Possible specific
indicators for search engines and web directories are:
·
frequency of key words used by visitors to find the site.
These key words can be identified by analysing the log
files, as the URL from which the visitor accesses the
search engine contains the key word used;
· positioning ij , i.e. the position5 of the site in the results
of the ith search engine for a query with the jth key
word. This is an indicator of the efficiency of the
subscription by the company. The main problem is
the time horizon for which the indicator is valid, as the
position can vary frequently, meaning that the monitoring must be almost continuous. The indicator can be
measured by querying the search engine using the key
words in question, or with special software which verifies
automatically the position in various engines.
Table 1. CPM and CTR measurement for search engines and web directories
CPM ˆ (BDG=N impression ) 1:000 The numerator (BDG) can be calculated as the purchasing cost of the key words, as the cost of the
man-hours necessary to subscribe to the search engines, or as the purchasing cost of the
software for automatic subscription.
The demoninator (Nimpression ) is difficult ot measure since it is not known how often the search
engine user views the site in the query results. It can be calculated indirectly by the number of
search engine users querying with a specific key and the position of the site when using this key
word.
CTR ˆ Nvisitors =N impression
The numerator (Nvisitors ) can be calculated by analysis of the log files
Table 2. CPM and CTR measurement for banners
CPM ˆ (BDG=N impression ) 1:000
Defined when the banner
campaign is purchased
CTR ˆ N visitors =Nimpression
Analysis of log files
The following section describes relationships between
the basic components of the cash outflows and the threemacro variables of the e-commerce mix: website, products
and services and promotion.
The website influences:
·
Indicators for Cash Outflows
Products and services influence:
·
fixed costs of customer care, as interactive services and
pre- and after-sales support vary in function of the type
of product range. Costs for personnel to manage these
services fall into this category if software for automatic
management is not used;
· variable product costs, i.e. production costs of costs of
purchases from suppliers;
· delivery costs for packaging and dispatch, which depend
on the type of product and the stock management, itself
determined by the breadth and depth of the product
range.
Table 3. CPM and CTR measurement for e-mail based tools
CPM ˆ (BDG=N impression ) 1:000
The numerator can be calculated as the cost to acquire the e-mail addresses (for direct mailing),
or as the cost to sponsor the newsletter or mailing list, or as the cost of the man-hours
expended to set up and maintain a customer database.
The denominator can be calculated as the number of e-mails sent (for direct mailing), or as the
number of subscriptions to the newsletter or mailing list (for sponsorships). Newsgroups are
difficult to measure, as the number of users is not known.
CTR ˆ N visitors =Nimpression
The numerator is calculated as the number of accesses to the access page, as shown in the log
files.
Planning and Controlling a BtoC E-Commerce Strategy
The indicators for cash outflows are the variable and fixed
cost sustained by the company for the start-up and
management of the virtual store. In this case, too, the
three-macro variables of the e-commerce mix (website,
products and services and promotion) can be used, establishing for each the cost items. These costs must be
defined ex ante in the budget phase (standard costs), and
subsequently monitored in order to identify any changes.
Figure 5 shows the breakdown of cash outflow and the
relationships between the basic components of the cash
outflow and the three-macro variables of the e-commerce
mix.
Rangone
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Banners. Other tools based on e-mail. A possible specific
indicator for tools based on e-mail is the percentage of
protest mail. This can be calculated as the ratio between
the number of protest e-mails and the total number of emails sent through a given promotional channel, so assessing the company’s respect of netiquette. The only problem with the measurement is identification of the protest
e-mails regarding a specific advertising campaign, so that
the numerator and denominator remain coherent.
initial investment for graphical design and production
of the web pages, integration between the web pages
and the e-commerce software, purchase of software
licences and hardware, etc.;
· fixed costs of site management, i.e. residence and maintenance costs, personnel costs for ordinary updates, and
for order processing;
· fixed costs of customer care, determined by the use of ad
hoc software for the automatic management of the
interactive services offered on the site;
· variable costs in function of the number of financial
transactions, i.e. costs for credig card payment management, order processing costs, etc.
Figure 5. Breakdown of cash outflows
140
Finally, promotions determine the following cost items:
·
fixed promotional costs, i.e. costs for marketing personnel
and for advertising campaigns in various channels, etc.;
· variable promotional costs with regard, for example, to
affiliations, promotional campaigns paid on the basis of
sales generated (e.g. banner campaigns, etc.).
The indicators to monitor outgoing cash flows involves
various cost items. As stated above, these must be constantly monitored to identify any changes with respect to
budgeted figures.
CONCLUSIONS
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Analysis of the literature reveals an absence of a framework
to integrate decision-making variables and indicators that
can be used by a company in running a BtoC e-commerce
business. The e-commerce mix framework described in the
previous sections provides a systemic view of the decisionmaking variables and the performance indicators available
to management. Specifically, the framework has two
objectives:
1. to support management in the planning and decisionmaking process;
2. to control the effects of action on decision-making
variables, so initiating a learning porocess that might be
critical in future decisions.
Electronic Markets Vol. 10 No 2
Notes
141
1. Note that ‘pricing’ variable is considered within the
‘products and services’ variable.
2. See Appendix 1.
3. These are linguistic values belonging to the linguistic
variable W (cf. Step 3).
4. For example, Site Mapper, Astra site manager, InfoWeb,
Riadalinx, Xenu’s link sleuth.
5. The term refers to the number of pages the user has to
turn in the search engine results before reaching the
company’s link.
6. In the present case, the alternatives refer to different
virtual stores analysed in the benchmarking phase.
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APPENDIX 1
Notes on Fuzzy Set Theory
Fuzzy Set Theory has been applied to numerous company
problems with non-probabilistic uncertainty essentially due
to the vague and ambiguous definition of the relevant
variables (Karwowski and Evans 1986; Zimmermann
1991; Azzone and Rangone 1996; Rangone 1997). Zimmer, for instance, has asserted that humans tare unsuccessful in making quantiative predictions, whereas they are
more efficient in qualitiative predictions (Zimmer 1983).
For present purposes, attention is focused on two typical
assessment models based on Fuzzy Set Theory and used to
evaluate the effectiveness of a virtual store. These are:
Fuzzy Linguistic Models
Fuzzy linguistic models are based on the concept of
linguistic variables. A linguistic variable is a variable for
which the values are not numbers, but words or phrases of
a natural language (Zimmermann 1991). A linguistic
variable represents the group of its linguistic values, as
each value is a fuzzy set.
There are numerous fuzzy linguistic models (cf. for
example, Liang and Wang 1993; Rangone 1997). The one
used to assess the effectiveness of a virtual store is built in
the following steps:
1. definition of the importance weightings of the assessment criteria; the importance of the criteria can be
measured by the linguistic variable X ;
2. attrition of the values for each assessment criterion in
the alternatives analysed;6 attribution is by the linguistic
variable W ;
3. calculation of the overall assessment of each individual
alternative, weighting the values attributed to the
respective importance criteria and ranking the alternatives. The overall value of the assessment can be
calculated with the following weighted average operator
(Zadeh 1965):
Planning and Controlling a BtoC E-Commerce Strategy
· fuzzy linguistic models;
· heuristic models based on fuzzy logic.
Rangone
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142
K
i
ˆ (1=m)
K
[(Ai1
(A im
W 1)
(Ai2
W 2)
(Ai3
W 3)
W m )]
where:
K i is a fuzzy number representing the overall value
obtained in the assessment of the ith alternative; W J is
the importance of the jth criterion; A ij is the linguistic
value associated to the ith alternative for the jth
criterion, and m is the number of assessment criteria;
‘ , ’ are the fuzzy algebraic addition and multiplication operators.
By the Zadeh’s extension principle, the extended algebraic operations on triangular fuzzy numbers used in the
framework are as follows:
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(a 2 , b 2 , c 2 ) ˆ (a1 ‡ a 2 , b 1 ‡ b 2 , c 1 ‡ c 2 )
Multiplication
(a1 , b 1 , c 1 )
(a 2 , b 2 , c 2 ) 
(a 1 a 2 , b 1 b 2 , c 1 c 2 )
Division
(a1 , b 1 , c 1 ) Æ
(a2 , b 2 , c 2 ) 
(a 1 =c 2 , b 1 =b 2 , c 1 =a 2 )
(1, 1, 1) Æ
Electronic Markets Vol. 10 No 2
U i2 ˆ
Ni
X
Yi ˆ
Ni
X
Qi ˆ
Ni
X
Zi ˆ
Ni
X
( pij - oij )(b ij - a ij )=N i
iˆ1
iˆ1
( pij (aij - b ij ) ‡ b ij (oij -
pij ))=N i
q ij c ij =N i
j ˆ1
oij a ij =N i
j ˆ1
pij b ij =N i
j ˆ1
H i1 ˆ T i2 =2T i1
The formula gives a fuzzy number with a non-triangular
membership function that can be approximated to a fuzzy
triangle whose vertices coincide with the weighted average
of the assessments of the various alternatives: K i ˆ
(Y i , Q i , Z i ), (Zadeh 1965).
On the basis of K i , the alternatives analysed can be
ranked. The literature proposes different methods, including that based on the centre of gravity of the fuzzy
number K i , calculated as:
…
xdS i
Si
…
X Gi ˆ
dS i
Si
Reciprocal
143
Ni
X
H i2 ˆ - U i2 =2U i1
Addition
(a 1 , b 1 , c 1 )
U i1 ˆ
(a, b, c) 
(1=c, 1=b, 1=a)
The membership function of K can be calculated by the
formula proposed by Zadeh. By the extension principal,
K i is a fuzzy number with the following membership
function:
8
>
- H i1 ‡ [ H 2i1 ‡ (x - Y i )=T i1 ]1=2
>
>
>
>
>
>
>
Yi < x < Q i
>
>
<
K i (x) ˆ H i - [ H 2i2 ‡ (x - Z i )=U i1 ]1=2
>
>
>
>
>
>
Q i < x < Z i i ˆ 1, 2, KM
>
>
>
>
:
0
otherwise
T i1 ˆ
Ni
X
(aij - q ij )(a ij - c ij )=N i
T i2 ˆ
Ni
X
(q ij (a ij - c ij ) ‡ c ij (aij - q ij ))=N i
iˆ1
iˆ1
Heuristic Models Based on Fuzzy Logic
The models in this category are based on the concept of
linguistic decision-making rules ( fuzzy) formulated in the
following way (Dubois and Preade 1984; Lee 1990; Kosko
1993):
IF (a series of conditions is satisfied)
THEN (a consequence can be deduced):
A fuzzy rule can be considered as a fuzzy conditional
expression (Lee 1990), in which the condition (IF) expresses a combination of values assumed by the indicators,
while the consequence (THEN) represents the corresponding assessment.
This model has been used to transform the values of the
On/Off indicators into linguistic values belonging to the
linguistic variable W .