iBusiness, 2013, 5, 205-208
http://dx.doi.org/10.4236/ib.2013.53B041 Published Online September 2013 (http://www.scirp.org/journal/ib) 205
E-Commerce Business Models and Search Engine
Dependency
Tobias Klatt
Department of Business Administration and Economics, European-University Viadrina, Frankfurt Oder, Germany.
Received 2013
ABSTRACT
E-Commerce business models attracted a great deal of attention in the last years. An increasing number of bargains are
realized via online transactions. However, some business models suffer distinctly under changes of search engine algo-
rithms while others experience continuous stable traffic. This paper sheds light on the drivers of the unpunished
e-commerce businesses based on a case-by-case analysis of 43 business models in the German Internet market. The
analysis reveals that more stable business models are characterized by diversified customer arrivals which are obtained
by a focused product management, multiple marketing ch anneling, freemium registration strategies and a subtle way to
attract customer trust.
Keywords: E-Commerce; Digital Business Models; Search Engine Optimization; Transaction Costs
1. Introduction
The digitalization of markets sets the stage for the evolu-
tion of new e-commerce platforms, sales channels, and
services. Academic research accompanied this evolution
with plentiful insights on advices for th e best practices of
business models[1]. However, the multitude of proposi-
tions and the increasing environmental dynamism awak-
ened a certain degree of uncertainty about the design and
management of those businesses [2].
Frequently, the success of digital businesses depends
on their listings in search engine result pages (SERP) [3].
These result pages represent more than pure information
and frequently build awareness and push brand strength
[4]. Unfortunately, SERP are frequently affected by
changes in the particular search algorithms, such as the
mysterious Panda updates by Google. In the consequence
to these updates, some businesses suffer immediately
while others experience stable traffic or even benefit
from these changes.
This article addresses the uncertainty associated with
changes in SERP listings. Evidence on the effects of
changing search algorithms and strategies to reduce the
dependency on SERP are revealed within an initial inter-
view round with SEO experts and a follow-up case
analysis of 43 business models in the German Internet
market.
The following section outlines the research back ground
which focuses on SERP importance and changes in
transaction costs caused by adjustments in search al-go-
rithms. Section three is dedicated to the case analysis and
discusses the implications. Section four gives conclusions,
limitations and goals for further research.
2. Theoretical Background
2.1. SERP Importance and Control
Search engines have developed from a disregarded medi-
ating role into one of the most prominent pages in the
web. Today, they represent the gate to the Internet in the
presence of multitudinous forums, platforms and shops
[5]. More than half of all visitors to websites arrive there
from search engines rather than through a direct link [6].
Consequently, search engine advertising (SEA) becomes
increasingly important. This channel will soon capture a
lion’s share of the online advertisi ng pi e [7] .
The importance of search engines increases, further-
more, with the spill-over of branding effects and cus-
tomer trust in the ranking of SERP. The rank of web
pages in the search results influences directly consumer
click behavior [8]. Studies have shown that users have
even more trust in organic listings with higher conversion
rates than in SEA campaigns [9]. Consequently, compa-
nies push the rankings of their websites higher in organic
search results through different techniques of search en-
gine optimi zat ion (S EO ).
However, these SEO activities are frequently equ alized
by adjustments in search algorithms. These changes are
made for ambiguous purposes, such as technological im-

E-Commerce Business Models and Search Engine Dependency
206
provements or for suspending low utility pages. Never-
theless, each adjustment changes the SERP and the re-
lated branding and transaction cost effects.
2.2. A Transaction Cost Problem
Search engines grew to support the access to the enor-
mous information on the Internet by crawling, retrieving,
and presenting relevant information for users based upon
their search algorithms [10]. These engines thereby di-
rectly impact on the user’s search costs which represent
one aspect of the costs involved in online transactions of
e-commerce business models.
Transaction costs are one if not the critical factor that
companies doing business over the Internet try to reduce
[11]. Besides information costs, search engines also af-
fect agency costs and transaction uncertainty.
Agency costs emerge in the presence of various ven-
dors that seem to offer nearly the same product or infor-
mation. Unknown brands benefit in the presence of as-
similation effects that stipulate users to reshape their
perceptions and elevate unknown brands along the
primed brand attributes [12]. In these cases, changes in
the ordering of search results can simply change transac-
tion partners .
This randomness of customer choices creates a certain
level of transaction uncertainty for digital companies.
Online businesses can not rely on certain click-through
and subsequent conversion rates of customer arrivals
from SERP. Therefore, adjustments in search algorithms
represent a substantial risk to those business models that
mainly rely on conversion from SERP.
3. Case Analysis
3.1. Consequences of Search Algorithm Changes
Preliminary interviews with SEO experts confirmed the
significance of search algorithm changes. Search engines
use continuous as well as drastic updates of their search
algorithms, such as the most prominent Jagger, Panda or
Penguin updates. Generally, experts assume more than
500 incremental algorithm changes per year and only the
striking ones are reported in the community [13].
The impact of these updates on website traffic is un-
certain. Some SEO experts reported traffic drops of more
than 50 percent in their company while other businesses
were not affected. Similarly, the rebuilding of the af-
fected websites is an art in itself. Even experienced SEO
specialists have to find new ways in the presence of un-
certainty about the direction of algorithm changes. Bing’s
webmaster comes straight to the point of this uncertainty
and emphasizes the necessity for a broader understanding
of website construction:
“You cannot control when a search engine makes an
update, or what that update will impact. That much is
obvious. But wha t many websites fail to take ac tion on is
forecasting change, preventative work and exercises in
the obvious.” [14]
The following case analysis reveals evidence about
strategies of digital businesses that perform such a pre-
ventative work better than businesses which are more
affected by changes in SERP.
3.2. Research Design
The analysis is based on a longitudinal case research de-
sign of different e-commerce business models. E-com-
merce companies are defined as firms that derive a sig-
nificant proportion of their revenues by participating in
transactions over the Internet [15]. This study tightens
this definition and considers only pure plays, i.e. digital
businesses in terms of delivering either physical or virtual
goods and services to the customer purely based on
transactions facilitated by the In ternet.
Furthermore, the selection is restricted to e-commerce
firms founded in Germany. This regional focus should
avoid biases from institutional differences and time lags
owing to the regional focus of search algorithm updates
which are launched at different times over the world.
Initially, a set of the ten most affected companies from
the prominent Pand a 2011 update were chosen according
to the analysis of searchmetrics [16], see Table 1. Their
business models are contrasted against the 33 most
prominent German digital businesses judged by the Ger-
man entrepreneurship community [17], see Table 2. Data
about the 43 companies were acquired from public
sources and analyzed using standard within-case and
cross-case analysis [18].
Notes were taken on the business focus, the segment
and the used marketing channels during the initial
within-case analysis. In the cross-case analysis the results
of the 10 affected companies were contrasted with the
results of the 33 successful companies.
Table 1. 10 most affected companies by Panda updatea.
Company Business Segment Marketing Channelsb
Ciao Price checkRetail A, B
Cosmiq CommunityNetwork A, B
Dooyoo Price checkRetail A, B
Gutefrage CommunityNetwork A, B, C
Helpster CommunityNetwork A, B
Ladenzeile Price checkRetail A, B, C
Suite101 Magazine Media A, B
Wer-weiss-wasCommunityNetwork A, B
Wikio Price checkRetail A, B
Yopi Price checkRetail A, B
aAccording to Searchmetics [16]. bUsing SEO (A), SEA (B), other forms of
massive online advertising (C), print advertising (D) and radio and TV cam-
paigns (E).

E-Commerce Business Models and Search Engine Dependency 207
Table 2. 33 most successful digital companies in Germanya.
Company Business Segment Marketing Channelsb
Amiando Ticketing Retail A, B, C
Barcoo App Services A, B
Betterplace Community Network A, B, C, D
Bigpoint Gaming Network A, B
Brands4Friends Clothing Retail A, B, C, D
Buch.de Books Retail A, B, C, D
DaWanda Uniques Retail A, B, C
Direktzu Community Network A, B
Dress-for-Less Clothing Retail A, B, C
GameDuell Gaming Network A, B, C
Gameforge G aming Network A, B, C
Groupon Shopping Retail A, B, C, D, E
Immoscout Real estate Retail A, B, C, D, E
Internetstores Deliveries Retail A, B, C
Mymuesli Cereals Retail A, B, C, D
Niiu News Media A, B
PaperC Books Retail A, B
Parship Dating Network A, B, C, D, E
Pizza.de Food Retail A, B, C, D
Qype Community Network A, B, C
SchülerVZ Community Network A, B, C, D
SoundCloud Music Retail A, B, C
Spickmich Community Network A, B
Sport1.de Information Media A, B, C, D, E
Spreadshirt Clothing Retail A, B, C
Teekampagne Tea Retail A, B, C
Travian Gaming Network A, B
Trivago Price check Retail A, B, C, D, E
Web.de Information Services A, B, C, D
Wooga Gaming Network A, B
Xing Community Network A, B, C, D
Zalando C loth ing Retail A, B, C, D, E
Zanox Marketing Services A, B, C, D
aAssessed by the German entrepreneurship community on the basis of eco-
nomic success, innovativeness, utility, reach and pioneer [17]. bUsing SEO
(A), SEA (B), other forms of massive online advertising (C), print advertis-
ing (D) and ra dio and TV campaigns (E).
3.3. Results and Implications
The company overview shows at first sight a heteroge-
neous picture among the business models. Social com-
munities, such as SchülerVZ or Direktzu, have little in
common with ticket stores, cloth ing shops or the affiliate
marketing network Zanox. Nevertheless, the cross-case
comparison reveals some striking differences among the
two company sets which allow fo r some insights on driv-
ers of search engine independency and successful strate-
gizing in e-commerce business models.
Diversified arrivals: Most of the 33 successful com-
panies rely on diversified customer arrivals. More versa-
tile market cultivation activities seem to attract more
customers from third-party websites and direct links,
thereby. In contrast, the ten affected companies were hit
that severe by the Panda update because of their high
dependency on customers following SEA and SEO cam-
paigns from SERP. An option to avoid this risk is to di-
versify customer arrivals through the following best prac-
tices. These strategies are in line with the advice of
Bing’s webmaster who recommended preventative work
as an antidote to search engine dependency.
Focused products: Most of the successful companies
are characterized by one or few distinctive products.
Even communities, such as the career network Xing or
the student community SchülerVZ, clearly address a par-
ticular customer sub-category in contrast to general
communities that try to address everyone, such as
Gutefrage or Wer-weiss-was. The prominent paradigm of
focusing on core competencies holds as well for e-com-
merce business models.
Multiple channeling: The successful companies use a
multitude of information channels to reach customers.
They address potential consumers mostly through a wide
range of marketing channels. Furthermore, regular cus-
tomers are continuously informed about new services,
frequently through customized newsletters, and special
offers. Continuous information help to stay in contact
with customers, shape trends and promote new brands.
Subtle trust. Confidence in online shops and commu-
nities is a core problem of newcomer businesses. How-
ever, the within-case analysis revealed that the successful
e-commerce businesses use a subtle way to cause con-
sumer trust. A frequently used instrument is the aban-
donment of advertisements on their websites. Companies
use this simple principle to create a trustful platform for
their product sales, such as the tea seller Teekampagne.
The punished companies, by contrast, exhaust the reve-
nue stream opened by skyscrapers and other ads.
Freemium registrations: This business model is not a
new insight but it still possesses strong power to avoid
search engine dependency. Communities, such as Better-
place, Qype or Spickmich, use a simple and short free
registration form to tie customers within their platform.
Some companies even try to skim revenue through offer-
ing premium registratio n upgrades, e.g. Xing. In contra st,
the punished companies, such as Ciao or Ladenzeile, of-
fer their price check service without any registration and
try to earn money solely through advertisements and
cost-by-click.
Recommended references: A further simple instru-
ment to attract customer arrivals via other sources than
search engines rests in customer recommendations. Suc-
cessful companies are characterized by simple and un-

E-Commerce Business Models and Search Engine Dependency
208
disturbing hints for posting and sending recommenda-
tions or inviting friends. The punished companies skip
the recommendation opportunity for the price of ad-
dressing the whole Internet community openly which
seems an inadequate strategy in times of increasing com-
petition and specialization of e-commerce business mod-
els.
4. Conclusions
This case analysis reveals evidence on strategies to avoid
a strong dependency on search engine arrivals and the
consequent risk of traffic losses due to changes in search
algorithms. Based on SEO expert interviews and a longi-
tudinal case study in the German e-commerce market
insights on best practices of successful digital companies
are presented. Besides the general strategy of customer
arrival diversification, the case analysis shows that suc-
cessful e-commerce companies use multiple instruments
to comprehensively attract customers through direct links
and third-party websites.
These strategies help to reduce transaction and agency
costs and transaction uncertainty. Of course, stronger
effort and even higher marketing costs are necessary to
grow businesses following these strategies. And of course,
there are still examples for other e-commerce models that
remain unaffected by search algorithms changes despite
ignoring the strategies. However, they may be affected by
the next search engine updates.
Further research should concentrate on detailed dis-
tinctions of new e-commerce business models and asso-
ciated competitive strategies. We can expect that the
digital market will further differentiate and create new
sales channels. Moreover, a new research stream is at the
starting blocks to reveal insights on e-commerce via
smartphones and special offers for tablets which require
different marketing channels and business models.
5. Acknowledgements
The au thor than ks Ale xande r Drees, Benjamin Feldmann,
Philipp Appelt and Martin Loske for substantive insights
into new e-commerce businesses and helpful comments.
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