E-Commerce Business Models and Search Engine Dependency

doi:10.4236/ib.2013.53B041

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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.

Email: [email protected]

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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