The paper points out the limits of two empirical
studies on the value of direct navigation. To more accurately
predict the value created through direct navigation, these issues
must be addressed.
Direct navigation has tremendous marketing implications and is of paramount interest to buyers and sellers of Internet
properties (domain names and websites). Thus, it is imperative
that the sources and magnitude of additional value creation are
are two major empirical studies on direct navigation. The first,
by WebSideStory, finds that sales-conversion
rates when a visitor arrives at a site through direct navigation
are higher than other forms of Internet navigation. The second considers only the relationship between direct navigation and
its impact on domain name prices and parking revenue.
was the first to use sales-conversion rates as a proxy for comparative
navigational revenue. The proxy is an intelligent choice for website
profits, which are not publicly available. The study also developed
an interesting visitor classification model. Earlier studies used
comparative traffic volumes across various Internet navigational
vehicles, which lacked any measures of profits.
studies have limitations that are detailed below.
study classifies visitors into three levels, and estimates the
conversion rates for each level and across various product groups.
However, the study has a number of shortcomings with regard to
visitor classification modeling, estimation techniques, and interpretations
quotes Ali Behnam, WebSideStory senior digital marketing consultant, providing a description of these variables.
Level two consists of “people
who have an idea of what they want, are in the market for a specific
product, but don't necessarily know where they want to get it.” Level three, on the other hand, consists of “people who know what they're looking for, and
know where they want to shop to get it.”
1. Classification Modeling
a. From the perspective of modeling visitors, the study’s verbal
classifications of variables and states can be translated into
the 2x2 framework below. Although such models are simplifications,
they are a very powerful analytical tool.
||Know What They Want
Where to Go
Thus, the framework
results in four disjoint visitor levels.
The level 4 group,
however, is not mentioned in the study! Compulsive shoppers, for
example, fall in this category, whereby they may regularly visit
a favorite discount/bargain website.
b. The study only considers same-session conversions, which makes
the distinction between direct navigation and search engine transactions
blurry. For example, the study cannot tell whether a direct navigation
had originated with a search engine visit. In such a case, search
engine conversion rate would be biased downward. Conversely, it does not account for the fact that a visitor
might have started the search process with direct navigation,
but, say, did not find the intended site and decided to use a
search engine. Ironically, arriving at a search engine site, on
the second leg of the search, would most likely be through direct
can be formulated into the following investigative question: What
is the conversion ratio for each of the visitor levels?
To answer the above question, there are two methodological
approaches: using a controlled experiment setting and data mining
techniques. The latter
is the approach adopted by the study.
a. Controlled Experiment Approach
An experiment can be
conducted in which visitors’ knowledge of where to go and what
they want can be explicitly controlled using a focus group. Nevertheless,
the limitation of this approach is that while it can control visitor
types, unless the focus group’s participants make purchases too,
estimated sales-conversion rates can be suspicious, as the focus
group’s items that they intend to purchase and purchasing the
items need not correlate.
b. Data Mining Approach
The study is based
on tracking activities of about 30 business-to-consumer ecommerce
sites over the last three months of 2005.
i. Measurement Error
In a data mining setting,
to estimate the sales-conversion rate for each visitor “what they
want” and “what they know” are held constant by the statistical techniques. In such an approach,
when control variables of the visitor classifications have to
be imputed, visitor classification becomes less accurate, and
thus, errors in control variables must be included in the
3. Interpretation of Results
a. Product Categories
behavior for five product categories is tested for differences
in sales-convergence rates. The study points out the importance
of “familiarity” of the site as a determinant in purchasing a
big-ticket item compared to, say, purchasing toys. But under level
3, it is assumed that the visitor knows where they are going.
Thus, what does “familiarity” represent for each of the categories
and how is it measured? Moreover, should level three visitors,
for example, be redefined based on the three characteristics “know
what they want,” “know where to go,” and “familiar with the site”?
If so, this familiarity factor is another variable that needs
to be imputed, adding to the problem of reliability of estimates.
the investigation into the reason for variations in conversion
rate across groups becomes interesting. One possible explanation is that for bigger ticket items,
the searcher bookmarks more sites or visits more sites through
type-ins than for small-ticket items. Thus, the conversion rate
is lower due to more bookmarks and/or type-ins for the big-ticket
items. This explanation makes sense, as the number of sites visited
for big-ticket items is probably higher due to potentially significant
b. Conversion Rates
on the conversion rate, while ignoring marketing costs, can be
misleading. Some of the type-ins and bookmarks have been paid
for by previous marketing campaigns. Thus, when such costs are
ignored, even with higher conversion rates, it does not necessarily
imply that direct navigation creates additional value to the visited
The study suggests that direct navigation does
not add value to a parked domain name. The lack of significance
of direct navigation can be attributed to statistical estimation
issues, as noted in the study, and that publicly available domain
name sale prices do not reflect the full benefits of direct navigation,
i.e., sellers, and possibly buyers, on average, do not fully understand
the value of direct navigation.