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Bachelorarbeiten Vergabeverfahren und Themen Lehrstuhl für Electronic Commerce Prof. Dr. Bernd Skiera Wintersemester 2017/2018
Bachelorarbeiten
Vergabeverfahren und Themen
Lehrstuhl für Electronic Commerce
Prof. Dr. Bernd Skiera
Wintersemester 2017/2018
Allgemeine Hinweise zu den Voraussetzungen zur Bearbeitung von Bachelorarbeiten
finden Sie unter:
http://www.wiwi.uni-
frankfurt.de/studium/studierende/pruefungsorganisation/allgemeine-
informationen/bachelorarbeit.html
Allgemeine Hinweise
Aktuelle Fristen finden Sie unter:
http://www.wiwi.uni-
frankfurt.de/studium/studierende/pruefungsorganisation/pruefungen/fristen.html
Fristen
Hinweise zum Bearbeiten von Bachelorarbeiten sowie eine Musterdatei des
Marketing Schwerpunkts finden Sie unter:
http://www.marketing.uni-frankfurt.de/studium/anleitung-zum-wissenschaftlichen-
arbeiten.html
Bearbeitungshinweise
Kontakt bei Fragen zur Vergabe der Bachelorarbeiten
RuW 1.218
Bewertungsvorlage
Ein erster Anhaltspunkt für die Benotung der Bachelorarbeiten am Schwerpunkt
Marketing ergibt sich aus folgendem Bewertungsschlüssel:
http://www.marketing.uni-
frankfurt.de/studium/bachelorarbeiten/bachelorarbeitsvergabe.html
Steffen Försch
Informationen
Melden Sie sich ab dem 12.10.2017 über das QIS-System für einen
Bachelorarbeitsplatz an. Wählen Sie hier als betreuenden Professor Prof. Dr. Bernd
Skiera aus.
1. Schritt: QIS Anmeldung
Wenige Tage nach Anmeldeschluss (ab dem 30.10.2017) erhalten wir von dem
Prüfungsamt die Liste aller erfolgreichen Anmeldungen. Wir werden Sie nun unter
Ihrer Studenten-Email-Adresse (@stud.uni-frankfurt.de) kontaktieren, um die
Vergabe der Themen zu koordinieren. Per E-Mail werden wir Ihnen das genaue
Vorgehen zur Vergabe der Themen detailliert erläutern. Die Details zur Vergabe der
Themen finden Sie auch auf der nächsten Folie.
2. Schritt: Themenvergabe
Kontakt bei Fragen zur Vergabe der Bachelorarbeiten
RuW 1.218
3. Schritt: Termin mit Betreuer
Vereinbaren Sie, zügig nachdem Ihnen Ihr Bachelorarbeitsthema mitgeteilt wurde,
einen Termin mit Ihrem Betreuer.
Steffen Försch
Bewerbung und Ablauf
Es gibt zwei Möglichkeiten für die Findung eines Bachelorarbeitsthemas:
1. Sie wählen ein vom Lehrstuhl vorgeschlagenes Bachelorarbeitsthema
(„Normalfall“)
Bitte treffen Sie in jedem Fall (auch wenn Sie ein eigenes Thema für Ihre
Bachelorarbeit vorschlagen möchten) unter den nachfolgend ausgeschriebenen
Themen ein Ranking Ihrer 5 Wunschthemen. Sie bekommen von uns, sofern
möglich, ein Thema gemäß Ihrer Themenpräferenzen zugeteilt.
2. Sie schlagen ein eigenes Thema für Ihre Bachelorarbeit vor
Wenn Sie ein eigenes Thema bearbeiten möchten, schicken Sie uns eine Datei in
der Sie kurz Ihren Themenvorschlag vorstellen. Erklären Sie dort (1) welches
Problem Sie lösen möchten, (2) warum Ihr Problem interessant ist und (3) wie Sie
das Problem lösen möchten (z.B. welche Daten Sie verwenden wollen). Ein guter
Grund für die Verwendung eines eigenen Themas ist beispielsweise eine empirisch
ausgerichtete Arbeit, die auf Daten aufbaut, die Ihnen zur Verfügung stehen. Wir sind
grundsätzlich auch bereit Bachelorarbeiten zu betreuen, welche zum Ziel haben, die
im Rahmen von Datamining-Wettbewerben ausgeschriebenen Problemstellungen zu
lösen (Beispiel https://www.kaggle.com/c/avazu-ctr-prediction).
Ihren Themenvorschlag werden wir am Lehrstuhl diskutieren. Wenn wir Ihr
vorgeschlagenes Thema für geeignet halten, können Sie es bearbeiten. Sollten wir
Ihr vorgeschlagenes Thema für ungeeignet halten, bearbeiten Sie das Ihnen vom
Lehrstuhl zugeteilte Thema.
Kontakt bei Fragen zur Vergabe der Bachelorarbeiten
RuW 1.218Steffen Försch
Themen für Ihre Bachelorarbeit
Ausgeschriebene Themen
Does a “Freemium” Strategy Help Firm’s Profit?
Over the past few years, “Freemium” (free + premium) strategy has become popular among firms,
especially among internet start-ups and smartphone app/game developers. In the freemium
strategy, users have access to the basic features at no cost and can access richer functionality for
a fee. For example, Dropbox offers a 2 GB cloud storage for free, yet, users can pay a fee to have
access to more cloud storage. Firms mainly decide to adopt a freemium strategy to increase the
number of their premium users and, consequently, increase profit. However, the decision to adopt a
freemium strategy is costly for the firm (incurred by free service to free users) and may come at the
expense of downgrading of premium users (to free service).
Among others, this thesis should: (1) summarize (also quantitatively) the existing literature on
freemium strategy, (2a) answer whether firms generally benefit from freemium strategy, and (2b)
answer which types of firms benefit from freemium strategy; for example, firms with “utilitarian
products” (for example, LinkedIn and Dropbox) or firms with “hedonic products” (for example,
Android mobile games).
Overview
This topic is only available in English.
Language
Contact
RuW 3.208
Literature
Lee, C. / Kumar, V. / Gupta, S. (2017), "Designing Freemium: Balancing Growth and Monetization
Strategies", Available at SSRN: https://ssrn.com/abstract=2767135.
Lambrecht, A. / Goldfarb, A. / Bonatti, A. / Ghose, A. / Goldstein, D.G. / Lewis, R. / Rao, A. /
Sahni, N. / Yao, S. (2014), "How do Firms Make Money Selling Digital Goods Online?", Marketing
Letters, 25 (3), 331-341.
Kumar, V. / Anand, B.N. / Gupta, S. / Oberholzer-Gee, F. (2012), "The New York Times Paywall",
Available at SSRN: https://ssrn.com/abstract=2053220.
Liu, C.Z. / Au, Y.A. / Choi, H.S. (2014), "Effects of Freemium Strategy in the Mobile App Market:
An Empirical Study of Google Play", Journal of Management Information Systems, 31 (3), 326-354.
Supervisor:
Iman Ahmadi / Prof. Dr. Bernd Skiera
Bachelor Thesis
• High interest in the topic
• Sufficient knowledge in marketing topics, and pricing models
Requirements
What are the Alternative Ways to Price Reduction for Retailers to
Increase Their Sales?
Retailers often reduce the prices of their products (for example, by discounting the prices) as a
strategy to attract more customers, increase the sales of their products, and eventually increase
their profit. Yet, selling products at lower prices does not always lead to increase in sales (and,
consequently, increase in profit) for the focal retailer. For example, reducing the prices of products
by a retailer may result in a price war (i.e., the retailer has to continuously reduce the prices in
response to further decrease in prices by focal retailers’ competitors). Therefore, retailers try to
implement other strategies, as an alternative to reducing the prices, to overcome the negative
consequences of price reduction. For example, REWE implemented ‘Unsere Erde’, a short-term
program that rewarded consumers instantly.
Among others, this thesis should: (1) summarize (also quantitatively) the existing literature on
consequences of implementing price reduction strategy in the retailing industry, and (2) identify,
classify, and discuss (also qualitatively) other alternative strategies that retailers could implement to
increase the sales.
Overview
This topic is only available in English.
Language
Contact
RuW 3.208
Literature
Harald J. van Heerde / Els Gijsbrechts / Pauwels, K. (2008), "Winners and Losers in a Major
Price War", Journal of Marketing Research, 45 (5), 499-518.
Minnema, A. / Bijmolt, T.H.A. / Non, M.C. (2017), "The Impact of Instant Reward Programs and
Bonus Premiums on Consumer Purchase Behavior", International Journal of Research in
Marketing, 34 (1), 194-211.
Kivetz, R. / Urminsky, O. / Zheng, Y. (2006), "The Goal-Gradient Hypothesis Resurrected:
Purchase Acceleration, Illusionary Goal Progress, and Customer Retention", Journal of Marketing
Research, 43 (1), 39-58.
Mägi, A.W. (2003), "Share of Wallet in Retailing: the Effects of Customer Satisfaction, Loyalty
Cards and Shopper Characteristics", Journal of Retailing, 79 (2), 97-106.h, 43 (1), 39-58.
Supervisor:
Iman Ahmadi / Prof. Dr. Bernd Skiera
Bachelor Thesis
• High interest in the topic
• Sufficient knowledge in marketing topics, and pricing models
Requirements
Online Display Advertising: What are the Available Opportunities
for Advertisers to Target Online Consumers?
Online advertising is one of the widespread ways that advertisers consider for their marketing
campaigns. Nowadays, due to progress in the technology, advertisers have the opportunity to
target online consumers, as opposed to “blindly” showing their advertisements to online consumers,
and increase their return on investment. For example, tracking technologies such as “cookies” allow
to collect detailed information about an online consumer’s browsing and shopping behavior, which
enable an advertiser to target specific online consumer (also known as ‘behavioral targeting’).
Alternatively, an advertiser could target an online consumer that is browsing on the page that has a
similar content to the one of the advertiser’s (also known as ‘contextual targeting’).
Among others, this thesis should: (1) summarize (also quantitatively) the existing literature on the
online display advertising, and (2) identify, classify, and discuss (also qualitatively) other alternative
ways that enable advertisers to target online consumers.
Overview
This topic is only available in English.
Language
Contact
RuW 3.208
Literature
Goldfarb, A. / Tucker, C. (2011), "Online Display Advertising: Targeting and Obtrusiveness",
Marketing Science, 30 (3), 389-404.
Hoban, P.R. / Bucklin, R.E. (2015), "Effects of Internet Display Advertising in the Purchase Funnel:
Model-Based Insights from a Randomized Field Experiment", Journal of Marketing Research, 52
(3), 375-393.
Yan, J. / Liu, N. / Wang, G. / Zhang, W. / Jiang, Y. / Chen, Z. (2009), "How Much Can Behavioral
Targeting Help Online Advertising?", Proceedings of the 18th international conference on World
wide web. Madrid, Spain, ACM, 261-270.
Ur, B. / Leon, P.G. / Cranor, L.F. / Shay, R. / Wang, Y. (2012), "Smart, Useful, Scary, Creepy:
Perceptions of Online Behavioral Advertising", Proceedings of the Eighth Symposium on Usable
Privacy and Security. Washington, D.C., ACM, 1-15.
Supervisor:
Iman Ahmadi / Prof. Dr. Bernd Skiera
Bachelor Thesis
• High interest in the topic
• Sufficient knowledge in marketing topics, and pricing models
Requirements
Wie präzise können Werbetreibende den Standort ihrer Kunden
ermitteln?
Über Lokalisierungsdienste, wie sie zum Beispiel von Google verwendet werden, können
Bewegungsprofile von Smartphone-Nutzern erstellt werden. Solche Bewegungsprofile werden von
Werbetreibenden gezielt verwendet um zum Beispiel location-based targeting zu betreiben. Im
Rahmen dieser Bachelorarbeit soll die Frage beantwortet werden, wie präzise der Standort eines
Nutzers durch Werbetreibende bestimmt werden kann. Dazu kann beispielsweise der eigene
Google Standortverlauf oder der von Freunden und Bekannten analysiert werden. Mögliche Punkte
auf die eingegangen werden kann sind:
• Wie kann die Güte der Lokalisierungsgenauigkeit definiert werden?
• Wie genau sind die Schätzungen bei verschiedenen Ortungs-Methoden? (WLAN / GPS)
• Wie genau kann der Besuch einer Filiale bestimmt werden?
• Für welche Anwendungen ist die Lokalisierung von Kunden denkbar?
Überblick
• Bereitschaft zum Sammeln und Auswerten von Standortdaten
• Erste Kenntnisse im Umgang mit einer Statistiksoftware (R, Stata)
• Kenntnisse im Bereich Electronic Commerce
Voraussetzungen
Englisch / Deutsch
Sprache
Kontakt
RuW 1.218
Literatur
Bauer, C. (2013), "On the (In-)Accuracy of GPS Measures of Smartphones: A Study of Running
Tracking Applications", Proceedings of International Conference on Advances in Mobile Computing
& Multimedia. Vienna, Austria, ACM, 335-341.
Molitor, D. / Reichhart, P. / Spann, M. / Ghose, A. (2016), "Measuring the Effectiveness of
Location-Based Advertising: A Randomized Field Experiment".
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2645281.
Polmonari, J. (2016), "Why Location Accuracy Matters",
http://www.huffingtonpost.com/advertising-week/why-location-accuracy-mat_b_11239264.html,
Stand: 13.03.2017
Betreuer:
Steffen Försch / Prof. Dr. Bernd Skiera
Bachelorarbeit
Welche Faktoren beeinflussen die Sichtbarkeit einer App in App-
Stores?
Aus der rasant wachsenden Zahl an Apps in den App-Stores, hat sich die App-Store-Optimierung
(ASO) entwickelt. Das Ziel von ASO ist dabei vergleichbar mit dem der Suchmachinen-Optimierung
(SEO) im Suchmachinenmarketing: die Sichtbarkeit der eigenen App in den App-Stores zu
erhöhen.
Ziel dieser Arbeit ist es zu untersuchen, welche Faktoren die Sichtbarkeit einer App in App-Stores
beeinflussen. Dazu sollen zunächst auf Basis eines Literaturüberblicks verschiedene Faktoren,
welche die Sichtbarkeit von Apps beeinflussen können, herausgearbeitet werden. Die Stärke des
Einflusses dieser Faktoren soll dann durch die Analyse eines selbst erhobenen Datensatzes
überprüft werden.
Überblick
• Erste Kenntnisse im Umgang mit einer Statistiksoftware (R, Stata)
• Kenntnisse im Bereich Electronic Commerce
• Bereitschaft mit Web-Crawlern zu arbeiten
Voraussetzungen
Englisch / Deutsch
Sprache
Kontakt
RuW 1.218
Literatur
Lim, S.L. / Bentley, P.J. (2013). "Investigating app store ranking algorithms using a simulation of
mobile app ecosystems", 2013 IEEE Congress on Evolutionary Computation, 2672-2679.
McIlroy, S. / Ali, N. / Hassan, A.E. (2016), "Fresh apps: an empirical study of frequently-updated
mobile apps in the Google play store", Empirical Software Engineering, 21, (3), 1346-1370.
Skiera, B. / Abou Nabout, N. (2013), "PROSAD: A Bidding Decision Support System for PRofit
Optimizing Search Engine ADvertising", Marketing Science, 32, (2), 213-220.
Betreuer:
Steffen Försch / Prof. Dr. Bernd Skiera
Bachelorarbeit
Nichts wie weg: Wie beeinflusst das Wetter die Reiselust?
Das Wetter beeinflusst weite Teile unseres alltags, wie etwa unser Einkaufverhalten oder unsere
Stimmung. So ist es naheliegend anzunehmen, dass das Wetter auch unsere Reiselust beeinflusst.
Ziel dieser Arbeit ist es zu untersuchen, wie verschiedene Wetter-Faktoren wie Niederschlag oder
Temperatur die Reiselust von Menschen beeinflussen. Dazu müssen Wetterdaten mit den
Informationen über die Webseiten-Aufrufe von Reiseagenturen und Fluglinien kombiniert werden.
Während die entsprechenden Wetterdaten am Lehrstuhl vorliegen, müssen die Informationen über
Webseitenaufrufe in der Reisebranche im Rahmen der Arbeit gesammelt werden.
Überblick
• Erste Kenntnisse im Umgang mit einer Statistiksoftware (R, Stata)
• Kenntnisse im Bereich Electronic Commerce
• Bereitschaft mit Web-Crawlern zu arbeiten
Voraussetzungen
Englisch / Deutsch
Sprache
Kontakt
RuW 1.218
Literatur
Cools, M. / Moons, E. / Creemers, L. / Wets, G. (2010), "Changes in Travel Behavior in Response
to Weather Conditions", Transportation Research Record: Journal of the Transportation Research
Board, 2157, 22-28.
Li, C. / Luo, X. / Zhang, C. / Wang, X. (2017), "Sunny, Rainy, and Cloudy with a Chance of Mobile
Promotion Effectiveness", Marketing Science, forthcoming
Steinker, S. / Hoberg, K. / Thonemann, U.W. "The Value of Weather Information for E-Commerce
Operations", Production and Operations Management, forthcoming
Betreuer:
Steffen Försch / Prof. Dr. Bernd Skiera
Bachelorarbeit
Post-Funding Performance of Crowdfunding Projects
Kickstarter, the largest reward-based crowdfunding website, has facilitated the raising of over $3.2
billion from 13.5 million people, funding over 130,000 projects. After a crowdfunding campaign has
successfully ended, many creators allow potential customers to place a pre-order to reserve a spot
in line while the first batch of the rewards is being manufactured. Pre-orders are mostly filled after
crowdfunding orders, but regularly offer a discount from the eventual retail price.
The student is provided with a data set covering 1,000 pre-order stores from crowdfunding
campaigns including information about the campaigns’ characteristics and performance. The goals
of this empirical work are (1) to identify whether pre-order stores allow creators to keep the
momentum going outside of crowdfunding platforms and (2) to identify the potential relationship
between campaign attributes/performance and the outcome of post-campaign funding.
Overview
• Experiences with the usage of professional statistics software (Stata, R)
• Willingness to work with web scraping technologies
Requirements
English / German
Language
Contact
RuW 1.236
Literature
Mollick, E. (2014), “The Dynamics of Crowdfunding: An Exploratory Study”, Journal of Business
Venturing, 29, (1), 1-16.
Belleflamme, P. / Lambert, T. / Schwienbacher, A. (2014), “Crowdfunding: Tapping the Right
Crowd”, Journal of Business Venturing 29, 585–609.
Mollick, E. / Kuppuswamy, V. (2014), “After the Campaign: Outcomes of Crowdfunding“, UNC
Kenan-Flagler Research Paper.
Supervisor:
Daniel Blaseg / Prof. Dr. Bernd Skiera
Bachelor Thesis
Gründerwettbewerbe als Erfolgsindikator?
Mehr als 150 verschiedene Gründerwettbewerbe haben sich in den letzten Jahren in Deutschland
entwickelt. Die vielversprechendsten Ideen und Geschäftsmodelle werden hier von einer
fachkundigen Jury ausgewählt und mit Geldpreisen von bis zu 100.000 Euro prämiert. Doch ist
eine solche Auszeichnung ein Indikator für den späteren Erfolg eines Unternehmens? Wie
entwickeln sich die Preisträger über die Zeit?
Ziel der Arbeit ist es, teilnehmende Unternehmen und Preisträger von einer großen Anzahl von
Gründerwettbewerben und deren Charakteristiken in Deutschland manuell sowie automatisiert über
Web-Scrapping zu erfassen und die Auswirkungen auf die Entwicklung der Preisträger über die
Zeit zu analysieren.
Überblick
• Erfahrung im Umgang mit Statistik-Software (Stata, R)
• Erfahrungen mit Web-Scrapping Technologien
Voraussetzungen
Englisch / Deutsch
Sprache
Kontakt
RuW 1.236
Literatur
Der Foo, M. / Wong, P. / Ong, A. (2005), “Do others think you have a viable business idea? Team
diversity and judges’ evaluation of ideas in a business plan competition”, Journal of Business
Venturing, 20, (3), 385-402.
Karlsson, T. / Honig, B. (2009), “Judging a business by its cover: An institutional perspective on
new ventures and the business plan“, Journal of Business Venturing, 24, (1), 27-45.
Semrau, T. / Werner, A. (2013), “How Exactly Do Network Relationships Pay Off? The Effects of
Network Size and Relationship Quality on Access to Start-Up Resources“, Entrepreneurship Theory
and Practice, 38, (3), 501-525.
Betreuer:
Daniel Blaseg / Prof. Dr. Bernd Skiera
Bachelorarbeit
Delivery Delays in Crowdfunding
Kickstarter, the largest reward-based crowdfunding website, has facilitated the raising of over $3.2
billion from 13.5 million people, funding over 130,000 projects. Though many projects on Kickstarter
have gone on to be successful for project creators regarding raising capital, first evidence suggests
that many projects take longer to deliver than creators estimate. But to date, there has been no
clear evidence about how long and why projects exceed the promised delivery dates.
The student is provided with a data set covering more than 300,000 crowdfunding campaigns from
the platform Kickstarter including information about the campaigns’ characteristics, results, and
campaign updates. The goals of this empirical work are (1) to identify the real delivery date using a
text mining approach to extract information from campaign updates, (2) to identify the potential
relationship between the setup and performance of campaigns and delivery delays.
Overview
• Experiences with the usage of professional statistics software (Stata, R)
• Willingness to work with text mining approaches
Requirements
English / German
Language
Contact
RuW 1.236
Literature
Mollick, E. (2013), “The Dynamics of Crowdfunding: An Exploratory Study”, Journal of Business
Venturing, 29, (1), 1-16.
Mollick, E. (2015), “Delivery Rates on Kickstarter“, SSRN Working Paper.
Mollick, E. / Kuppuswamy, V. (2014), “After the Campaign: Outcomes of Crowdfunding“, UNC
Kenan-Flagler Research Paper
Supervisor:
Daniel Blaseg / Prof. Dr. Bernd Skiera
Bachelor Thesis
Consumer Click Behavior on Sponsored versus Organic Search
Engine Results
Search engines are the most popular tools for consumers to search for information. Therefore,
firms try to appear on the search engine results to capture consumers‘ attention while searching in
search engines. While relevant firms to a keyword (i.e., searched term) appear automatically in the
organic search results, search engines also let firms to advertise themselves on a keyword and
appear on sponsored results. Therefore, it is interesting to analyze how consumers respond to
each of these type of search engine results, and investigate their interaction effect.
The aim of this thesis is firstly to summarize the findings of previous literature on factors which
affect the responsiveness of search results such as rank and type of results. In the second part of
the thesis, you are asked to empirically investigate how the presence of sponsored search results
affects the consumer click behavior for organic search results within a given data set.
Overview
• Mathematical understanding as well as coding skills in STATA or any other statistical software to
carry out the analysis.
Requirements
Englisch
Language
Kontakt
RuW 1.229
Literature
Agarwal, A. / Hosanagar, K. / Smith, M.D. (2011), “Location, Location, Location: An Analysis of
Profitability of Position in Online Advertising Markets,” Journal of Marketing Research, 48 (6), 1057-
73.
Ghose, A. / Yang S. (2009), “An Empirical Analysis of Search Engine Advertising: Sponsored
Search in Electronic Markets,” Management Science, 55 (10), 1605-22.
Jerath, K. / Ma, L. / Park, Y. (2014), "Consumer Click Behavior at a Search Engine: The Role of
Keyword Popularity", Journal of Marketing Research, 51(4), 480-486
Bachelor Thesis
Supervisor:
Elham Maleki / Prof. Dr. Bernd Skiera
Geo-Targeting in Online Advertising
Competition in a location (country, city, or region) is one of the major factors for managers who
want to choose a business location. Similarly, online marketing channels such as display
advertising can highly benefit from location-based targeting. Advertisers usually choose locations
which are reasonable for their business, meaning the locations their products and services are
offered and can be used. However, it has not been shown yet how competition in a geographic
area such as the market share of the strong competitors can affect the results of online advertising
KPIs.
To investigate the effect of competition in a geographic area on the performance of online
advertising, the student will be provided with a dataset on a display advertising campaign in various
geographic areas as well as information about the intensity of competition in those areas for the
focal advertiser
Overview
• Mathematical understanding as well as coding skills in STATA or any other statistical software to
carry out the analysis.
Requirements
Englisch
Language
Kontakt
RuW 1.229
Literature
Banerjee, S./ Dholakia, R.R. (2008), “Mobile Advertising: Does Location-Based Advertising
Work?”, International Journal of Mobile Marketing, 2(2), 68-74.
Banerjee, S. / Viswanathan, V. / Raman, K. / Ying,H. (2013), "Assessing Prime-Time for
Geotargeting with Mobile Big Data", Journal of Marketing Analytics, 1(3), 174-183
Unni, R. / Harmon, R. (2007), "Perceived Effectiveness of Push vs. Pull Mobile Location Based
Advertising", Journal of Interactive Advertising, 7 (2), 28-40.
Bachelor Thesis
Supervisor:
Elham Maleki / Prof. Dr. Bernd Skiera
Online Visibility of Firms in Desktop versus Mobile
Today’s world has experienced a tremendous shift to mobile usage. Although desktop devices have
not been completely replaced by mobile devices, the increase in mobile usage has definitely
changed the way firms compete to take consumer attention. However, it is still questionable if
online visibility of firms, i.e., being successful in capturing consumers’ attention on the Internet, on
desktop devices automatically means being visible for mobile users and vice versa. Having a
mobile-friendly website, as well as an application associated with the website for mobile devices
are few of the many criteria which can affect a website’s online visibility among mobile users.
In this bachelor thesis, you will be provided with a dataset including organic and paid search engine
results for desktop and mobile for the major keywords of a specific market. Using this dataset, you
are asked to investigate how estimated online visibility of websites differs across devices (desktop
versus mobile) and what drives such a difference between desktop and mobile.
Overview
• Mathematical understanding as well as coding skills in SQL to extract the data and STATA or
any other statistical software to carry out the analysis.
Requirements
English
Language
Literature
Agarwal, A. / Hosanagar, K. / Smith, M.D. (2011), “Location, Location, Location: An Analysis of
Profitability of Position in Online Advertising Markets,” Journal of Marketing Research, 48 (6), 1057-
73.
Drèze, X / Zufryden, F. (2004), “Measurement of Online Visibility and Its Impact on Internet
Traffic,” Journal of Interactive Marketing, 18 (1), 20-37.
Kamvar, M / Baluja, S. (2006), “A Large Scale Study of Wireless Search Behavior: Google Mobile
Search,” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 701-
709.
Bachelor Thesis
Contact
RuW 1.229Supervisor:
Elham Maleki / Prof. Dr. Bernd Skiera
Competitive Intensity in Online Display Advertising
To be able to offer users a wide range of “free content”, many websites rely on display ads to
generate revenue. Firms engage on their hand on a fierce competition to advertise their products
within the ad slots provided by these websites. While there are many factors that can influence the
competitive intensity for ad slots within a certain website, little is known about the importance of
each factor.
The goal of this thesis is to empirically investigate the determinants of competitive intensity in
display advertising. The data for the analysis will be collected by the student using digital marketing
intelligence platforms and may also be enriched using additional data sources.
Overview
• Knowledge of econometrics and statistical software, such as STATA or R
• Understanding of the basic principles of online display advertisement
Requirements
English / German
Language
Contact
RuW 1.236
Literature
Gummerus, J. / Liljander, V. / Pura, M. / Van Riel, A. (2004), "Customer Loyalty to Content-based
Web Sites: the Case of an Online Health-care Service", Journal of services Marketing, 18 (3), 175-
186.
Korula, N. / Mirrokni, V. / Nazerzadeh, H. (2016), "Optimizing Display Advertising Markets:
Challenges and Directions", IEEE Internet Computing, 20 (1), 28-35.
Lambrecht, A. / Goldfarb, A. / Bonatti, A. / Ghose, A. / Goldstein, D.G. / Lewis, R. / Rao, A. /
Sahni, N. / Yao, S. (2014), "How do Firms Make Money Selling Digital Goods Online?", Marketing
Letters, 25 (3), 331-341.
Supervisor:
Gabriela A. Werb / Prof. Dr. Bernd Skiera
Bachelor Thesis
What Drives the Success of Original Content Productions?
The consumption of visual entertainment has faced a significant shift with the advent of on-demand
content. Streaming service providers such as Netflix have transformed the landscape by making
countless blockbusters and premium Hollywood content readily available within the reach of a click.
Counting with a solid consumer base across several countries, the main streaming services have
recently set to creating and distributing their own content, some of which enjoyed great success
and reached far beyond their subscribers’ audience.
The goal of this thesis is to empirically investigate which factors drive the success of original
content productions. For the analysis, the student will collect data on original content productions
from streaming providers (e.g. Netflix, Amazon, Hulu) and user generated content, such as movie
ratings.
Overview
• Knowledge of econometrics and statistical software, such as STATA or R
• Programming skills and willingness to work with web scraping technologies
Requirements
English / German
Language
Contact
RuW 1.236
Literature
Ainslie, A. / Drèze, X. / Zufryden, F. (2005), "Modeling Movie Life Cycles and Market Share",
Marketing Science, 24 (3), 508-517.
Alba, D. (2017), "Netflix Is Killing It—Big Time—After Pouring Cash Into Original Shows",
https://www.wired.com/2017/01/netflix-investing-original-shows-finally-pays-off/, Stand: 22. March
2017.
Moon, S. / Bergey, P.K. / Iacobucci, D. (2010), "Dynamic Effects Among Movie Ratings, Movie
Revenues, and Viewer Satisfaction", Journal of Marketing, 74 (1), 108-121.
Davenport, T.H. / Harris, J.G. (2009), "What People Want (and How to Predict it)", MIT Sloan
Management Review, 50 (2), 22.
Supervisor:
Gabriela A. Werb / Prof. Dr. Bernd Skiera
Bachelor Thesis
Using Machine Learning to Rank Product Reviews
Many online marketplaces provide a space for collaborative product reviews, which give consumers
the unprecedented opportunity to learn from the previous experiences of many other consumers
before deciding on a purchase. As the volume of user generated content increases, online
marketplaces have the challenging task to discern which factors make a review useful for future
consumers as well as to decide on which reviews to show first.
The goal of this thesis is to answer the following question:
• Is there an optimal set of rules for online marketplaces to display their reviews?
To do so, you will analyze two datasets of online product reviews, using both text analysis and
machine learning algorithms.
Overview
• Knowledge of econometrics and statistical software, such as STATA or R
• Programming skills and willingness to learn and work with text analysis and machine learning
algorithms
Requirements
English
Language
Contact
RuW 1.236
Literature
Felbermayr, A. / Nanopoulos, A. (2016), "The Role of Emotions for the Perceived Usefulness in
Online Customer Reviews", Journal of Interactive Marketing, 36 (2016), 60-76.
Filieri, R. (2015), "What Makes Online Reviews Helpful? A Diagnosticity-adoption Framework to
Explain Informational and Normative Influences in e-WOM", Journal of Business Research, 68 (6),
1261-1270.
Ludwig, S. / de Ruyter, K. / Friedman, M. / Brüggen, E.C. / Wetzels, M. / Pfann, G. (2013),
"More Than Words: The Influence of Affective Content and Linguistic Style Matches in Online
Reviews on Conversion Rates", Journal of Marketing, 77 (1), 87-103.
Ngo-Ye, T.L. / Sinha, A.P. (2014), "The Influence of Reviewer Engagement Characteristics on
Online Review Helpfulness: A Text Regression Model", Decision Support Systems, 61 (2014), 47-
58.
Supervisor:
Gabriela A. Werb / Prof. Dr. Bernd Skiera
Bachelor Thesis
“Like Me” - I Will Monetize You: How Companies Could Identify
the Value of Facebook Likes
Facebook is one of the most important social media channels for companies. Companies can post
on their Facebook page and users can „like“ it. In sequence, companies can observe how many
likes the post at their Facebook page received. Although, the number of likes quantifies the social
engagement with the company, yet the monetary value associated with the Facebook likes is not
clear. If the companies could identify the monetary value of the Facebook like, they could use it to
calculate the return on investment for social media.
The goals of this work are (1) to identify existing methods to find value of Facebook likes, (2) to
create a numerical example to use the existing methods for finding value of a Facebook like, (3) to
identify the possibly drawbacks of the existing methods, and (4) develop a better method to
calculate value of Facebook likes.
Overview
• Motivation to conduct a study about social media
Requirements
English / German
Language
Contact
RuW 1.233
Literature
Edwards, J. (2013) „"What Is A Facebook 'Like' Actually Worth In Dollars?,” Business Insider,
http://www.businessinsider.com/what-is-a-facebook-like-actually-worth-in-dollars-2013-3?IR=T
Mochon, D. / Johnson, K. / Schwartz, J. / Ariely, D. (2017), "What Are Likes Worth? A Facebook
Page Field Experiment", Journal of Marketing Research, 54 (2), 306-317.
Zarella, D. (2012) “How to Calculate the Value of a Like,” Harvard Business Review,
https://hbr.org/2012/11/how-to-calculate-the-value-of
Bachelorthesis
Supervisor:
Namig Nurullayev / Prof. Dr. Bernd Skiera
An Analysis of Cohort-based Retention Rate for Retailers
The retention rate is one of the key performance metrics. It is essential for diagnosing whether
customers are still in touch with the company and continue to use their products or services or not.
Also, the retention rate is very critical for marketing in order to understant how loyal the customers
are and how customers are satisfied with the products or services the company. In practice,
companies try to allocate customers into different cohorts (the group of people who share some
common characteristics, e.g. first purchase happening at the same month), as well as run analysis
on the specific cohorts to calculate retention rate. In the retail setting it is of interest to analyze
whether there are differences in retention rates across different cohorts.
The goals of this work are (1) to analyze the data from a retailer, and (2) to bring some insights
about retention rate across cohorts.
Overview
• Basic knowledge of any statistical software – preferably R
• Motivation to conduct an empirical study
Requirements
English / German
Language
Contact
RuW 1.233
Literature
Bijmolt, T.H.A. / Leeflang, P.S.H. / Block, F. / Eisenbeiss, M. / Hardie, B.G.S. / Lemmens, A. /
Saffert, P. (2010), "Analytics for Customer Engagement", Journal of Service Research, 13 (3), 341-
356.
Fader, P.S. / Hardie, B.G.S. (2009), "Probability Models for Customer-Base Analysis", Journal of
Interactive Marketing, 23 (1), 61-69.
Schweidel, D.A. / Fader, P.S. / Bradlow, E.T. (2008), "Understanding Service Retention Within
and Across Cohorts Using Limited Information", Journal of Marketing, 72 (1), 82-94.
Bachelorthesis
Supervisor:
Namig Nurullayev / Prof. Dr. Bernd Skiera
Which Customer Metrics Do E-commerce Retailers Report?
Customer metrics are important for managing relationships with customers and maintaining
sustainable business. For example, the customer satisfaction metric might help to define which
customers are satisfied and which customers are unhappy with the company. These kind of metrics
are beneficial to evaluate the business from the customer perspective. Considering that such
metrics are important for managers of e-commerce retailers, they are also useful for external
stakeholders to improve their future decision making (e.g. investing into company or not).
Therefore, it is interesting to analyze whether e-commerce retailers report customer metrics, if so,
which metrics they report.
The goals of this work are (1) to analyze annual reports of e-commerce retailers, (2) to identify
which customer metrics they report, and (3) to summarize the findings to communicate insights.
Overview
• Motivation to conduct a study about e-commerce retailers
• Motivation to collect data
Requirements
English / German
Language
Contact
RuW 1.233
Literature
Bayer, E. / Tuli, K.R. / Skiera, B. (2017), "Do Disclosures of Customer Metrics Lower Investors’
and Analysts’ Uncertainty but Hurt Firm Performance?", Journal of Marketing Research, 54 (2),
239-259.
Farris, P. / Bendle, N. / Pfeifer, P.E. / Reibstein, D.J. (2015), "Marketing Metrics: The Manager's
Guide to Measuring Marketing Performance", New Jersey: Pearson FT Press
Gupta, S. / Zeithaml, V. (2006), "Customer Metrics and Their Impact on Financial Performance",
Marketing Science, 25 (6), 718-739.
Ofer Mintz / Currim, I.S. (2013), "What Drives Managerial Use of Marketing and Financial Metrics
and Does Metric Use Affect Performance of Marketing-Mix Activities?", Journal of Marketing, 77 (2),
17-40.
Bachelorthesis
Supervisor:
Namig Nurullayev / Prof. Dr. Bernd Skiera
Do Investors Care about Customers?
Customer metrics, like churn or acquisition costs, can represent valuable input for company
valuations. However, it is unclear to what degree professional investors and stock analysts
incorporate these metrics in their assessments of a firm’s fundamental value.
The aim of this thesis is to explore empirically whether investors and stock-analysts include
customers (and the related metrics) in their discussions during quarterly earnings conferences. The
student needs to create a small dictionary of customer related terms and run a simple textual
analysis (e.g. counting term frequencies). For this purpose, a large sample of conference call
transcripts will be provided.
Overview
• Basic skills in a programming language (preferable Python or R)
• Willingness to learn and apply basic text-mining techniques
Requirements
English / German
Language
Contact
RuW 1.233
Literature
Bayer E. / Tuli K. R. / Skiera B. (2017), ‘‘Do Disclosures of Customer Metrics Lower Investors’ and
Analysts’ Uncertainty but Hurt Firm Performance?“, Journal of Marketing Research, 54 (2), 239-259
Schulze C. / Skiera B. / Wiesel T. (2012), "Linking Customer and Financial Metrics to Shareholder
Value: The Leverage Effect in Customer-Based Valuation.“, Journal of Marketing, 76 (2), 17-32
McCarthy D. M. / Fader P. S. / Hardie B. G. S. (2017), ‘‘Valuing Subscription-Based Businesses
Using Publicly Disclosed Customer Data‘‘, Journal of Marketing, 81 (1), 17-35
Bachelor Thesis
Supervisor:
Maximilian Matthe / Prof. Dr. Bernd Skiera
How Should Sentiment in UGC be Measured? An Empirical Model
Comparison
User-generated content (UCG), like product reviews or feedback, represents a large source of data
available to marketing managers. While information from this data can potentially be insightful, it
requires new methods in order to be processed. In the recent years, a large variety of such
methods have been developed. Yet, facing the decision which method to chose, managers lack
guidance.
The aim of this thesis is to conduct a comparison of different text-mining methods, which aim to
quantify the sentiment in UGC. To achieve this, the student needs to research and choose at least
two different methods and briefly explain the underlying algorithm. In the empirical part, the
algorithms shall be implemented and compared on a sample of product reviews, which will be
provided to the student. Lastly, the student should discuss implications for marketing managers.
Overview
• Basic skills in a programming language (preferable Python or R)
• Willingness to learn and apply different text-mining algorithms
Requirement
English / German
Language
Contact
RuW 1.233
Literature
Archak, N. / Ghose, A. / Ipeirotis, P. G. (2011), “Deriving the Pricing Power of Product Features
by Mining Consumer Reviews”. Management Science, 57 (8), 1485-1509.
Das, S. R. / Chen M. Y. (2007), "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the
Web.“, Management Science, 53 (9), 1375-1388
Hu, M. / Liu, B. (2004), "Mining and Summarizing Customer Reviews.“, Proceedings of the tenth
ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2004, 168-
177
Bachelor Thesis
Supervisor:
Maximilian Matthe / Prof. Dr. Bernd Skiera
How Valuable is Big Data? A Discussion of the Business Impact of
Unstructured Data
Advances in storage capacity and processing power have started the current hype around Big
Data. Around 95% of this data is estimated to be unstructured, e.g. in text form. With a tremendous
growth in – potentially insightful – data available, many companies wonder if and how their
business can also profit from its analysis.
The aim of this thesis is to discuss, whether (and in which areas) insights from big, unstructured
data can really generate substantial business impact. For this purpose, the student is asked to
assess the related academic evidence from a marketers perspective.
More precisely, the student should summarize the literature on the analysis of different sources of
unstructured data (1), discuss their implications for marketing activities (2) and critically assess the
generated business impact (3).
Overview
• High interest in the topics “Big Data“ and “Data Mining“
Requirements
English / German
Language
Contact
RuW 1.233
Literature
Lamberton C. / Stephen A. T. (2016), “A Thematic Exploration of Digital, Social Media, and Mobile
Marketing: Research Evolution from 2000 to 2015 and an Agenda for Future Inquiry.“, Journal of
Marketing, 80 (6), 146-172
Liu X. / Singh P. V. / Srinivasan K. (2016), “A Structured Analysis of Unstructured Big Data by
Leveraging Cloud Computing“, Marketing Science, 35, 363-388
Ngai E. W. / Xiu, L. / Chau D. C. (2009), “Application of Data Mining Techniques in Customer
Relationship Management: A Literature Review and Classification.“ Expert systems with
applications, 36 (2), 2592-2602
Wedel, M. / Kannan P. K. (2016), "Marketing Analytics for Data-Rich Environments.“, Journal of
Marketing, 80 (6), 97-121
Bachelor Thesis
Supervisor:
Maximilian Matthe / Prof. Dr. Bernd Skiera
