Artificial intelligence · Data mining · Optimization

Research contributions from methods to applications

My publications span artificial intelligence, machine learning, data mining, optimization, and applied data science, with an emphasis on reproducible methods and open-source software.

Marketing Research

[1]	Michael Hahsler. arulesViz: Interactive visualization of association rules with R. R Journal, 9(2):163--175, December 2017. [ DOI | at the publisher ] Association rule mining is a popular data mining method to discover interesting relationships between variables in large databases. An extensive toolbox is available in the R-extension package arules. However, mining association rules often results in a vast number of found rules, leaving the analyst with the task to go through a large set of rules to identify interesting ones. Sifting manually through extensive sets of rules is time-consuming and strenuous. Visualization and especially interactive visualization has a long history of making large amounts of data better accessible. The R-extension package arulesViz provides most popular visualization techniques for association rules. In this paper, we discuss recently added interactive visualizations to explore association rules and demonstrate how easily they can be used in arulesViz via a unified interface. With examples, we help to guide the user in selecting appropriate visualizations and interpreting the results.
[2]	Michael Hahsler and Radoslaw Karpienko. Visualizing association rules in hierarchical groups. Journal of Business Economics, 87(3):317--335, May 2016. [ DOI | at the publisher ] Association rule mining is one of the most popular data mining methods. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. Sifting manually through large sets of rules is time consuming and strenuous. Visualization has a long history of making large amounts of data better accessible using techniques like selecting and zooming. However, most association rule visualization techniques are still falling short when it comes to a large number of rules. In this paper we present an integrated framework for post-processing and visualization of association rules, which allows to intuitively explore and interpret highly complex scenarios. We demonstrate how this framework can be used to analyze large sets of association rules using the R software for statistical computing, and provide examples from the implementation in the R-package arulesViz.
[3]	Thomas Reutterer, Michael Hahsler, and Kurt Hornik. Data Mining und Marketing am Beispiel der explorativen Warenkorbanalyse. Marketing ZFP, 29(3):165--181, 2007. [ at the publisher ] Techniken des Data Mining stellen für die Marketingforschung und -praxis eine zunehmend bedeutsamere Bereicherung des herkömmlichen Methodenarsenals dar. Mit dem Einsatz solcher primär datengetriebener Analysewerkzeuge wird das Ziel verfolgt, marketingrelevante Informationen ”intelligent” aus großen Datenbanken (sog. Data Warehouses) zu extrahieren und für die weitere Entscheidungsvorbereitung in geeigneter Form aufzubereiten. Im vorliegenden Beitrag werden Berührungspunkte zwischen Data Mining und Marketing diskutiert und der konkrete Einsatz ausgewählter Data-Mining-Methoden am Beispiel der explorativen Warenkorb- bzw. Sortimentsverbundanalyse für einen Transaktionsdatensatz aus dem Lebensmitteleinzelhandel demonstriert. Zur Anwendung gelangen dabei Techniken aus dem Bereich der klassischen Affinitätsanalyse, ein K-Medoid-Verfahren der Clusteranalyse sowie Werkzeuge zur Generierung und anschließenden Beurteilung von Assoziationsregeln zwischen im Sortiment enthaltenen Warengruppen. Die Vorgehensweise wird dabei anhand des mit der Statistik-Software R frei verfügbaren Erweiterungspakets arules illustriert.
[4]	Christoph Breidert and Michael Hahsler. Adaptive conjoint analysis for pricing music downloads. In R. Decker and H.-J. Lenz, editors, Advances in Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization, pages 409--416. Springer-Verlag, 2007. [ DOI | preprint (PDF) ] Finding the right pricing for music downloads is of ample importance to the recording industry and music download service providers. For the recently introduced music downloads, reference prices are still developing and to find a revenue maximizing pricing scheme is a challenging task. The most commonly used approach is to employ linear pricing (e.g., iTunes, musicload). Lately, subscription models have emerged, offering their customers unlimited access to streaming music for a monthly fee (e.g., Napster, RealNetworks). However, other pricing strategies could also be used, such as quantity rebates starting at certain download volumes. Research has been done in this field and Buxmann et al. (2005) have shown that price cuts can improve revenue. In this paper we apply different approaches to estimate consumer's willingness to pay (WTP) for music downloads and compare our findings with the pricing strategies currently used in the market. To make informed decisions about pricing, knowledge about the consumer's WTP is essential. Three approaches based on adaptive conjoint analysis to estimate the WTP for bundles of music downloads are compared. Two of the approaches are based on a status-quo product (at market price and alternatively at an individually self-stated price), the third approach uses a linear model assuming a fixed utility per title. All three methods seem to be robust and deliver reasonable estimations of the respondent's WTPs. However, all but the linear model need an externally set price for the status-quo product which can introduce a bias.
[5]	Michael Hahsler, Kurt Hornik, and Thomas Reutterer. Warenkorbanalyse mit Hilfe der Statistik-Software R. In Peter Schnedlitz, Renate Buber, Thomas Reutterer, Arnold Schuh, and Christoph Teller, editors, Innovationen in Marketing, pages 144--163. Linde-Verlag, 2006. [ preprint (PDF) ] Die Warenkorb- oder Sortimentsverbundanalyse bezeichnet eine Reihe von Methoden zur Untersuchung der bei einem Einkauf gemeinsam nachgefragten Produkte oder Kategorien aus einem Handelssortiment. In diesem Beitrag wird die explorative Warenkorbanalyse näher beleuchtet, welche eine Verdichtung und kompakte Darstellung der in (zumeist sehr umfangreichen) Transaktionsdaten des Einzelhandels auffindbaren Verbundbeziehungen beabsichtigt. Mit einer enormen Anzahl an verfügbaren Erweiterungspaketen bietet sich die frei verfügbare Statistik-Software R als ideale Basis für die Durchführung solcher Warenkorbanalysen an. Die im Erweiterungspaket arules vorhandene Infrastruktur für Transaktionsdaten stellt eine flexible Basis für die Warenkorbanalyse bereit. Unterstützt wird die effiziente Darstellung, Bearbeitung und Analyse von Warenkorbdaten mitsamt beliebigen Zusatzinformationen zu Produkten (zum Beispiel Sortimentshierarchie) und zu Transaktionen (zum Beispiel Umsatz oder Deckungsbeitrag). Das Paket ist nahtlos in R integriert und ermöglicht dadurch die direkte Anwendung von bereits vorhandenen modernsten Verfahren für Sampling, Clusterbildung und Visualisierung von Warenkorbdaten. Zusätzlich sind in arules gängige Algorithmen zum Auffinden von Assoziationsregeln und die notwendigen Datenstrukturen zur Analyse von Mustern vorhanden. Eine Auswahl der wichtigsten Funktionen wird anhand eines realen Transaktionsdatensatzes aus dem Lebensmitteleinzelhandel demonstriert.
[6]	Christoph Breidert, Michael Hahsler, and Thomas Reutterer. A review of methods for measuring willingness-to-pay. Innovative Marketing, 2(4):8--32, 2006. [ preprint (PDF) | at the publisher ] Knowledge about a product's willingness-to-pay on behalf of its (potential) customers plays a crucial role in many areas of marketing management like pricing decisions or new product development. Numerous approaches to measure willingness-to-pay with differential conceptual foundations and methodological implications have been presented in the relevant literature so far. This article provides the reader with a systematic overview of the relevant literature on these competing approaches and associated schools of thought, recognizes their respective merits and discusses obstacles and issues regarding their adoption to measuring willingness-to-pay. Because of its practical relevance, special focus will be put on indirect surveying techniques and, in particular, conjoint-based applications will be discussed in more detail. The strengths and limitations of the individual approaches are discussed and evaluated from a managerial point of view.
[7]	Michael Hahsler. Optimizing web sites for customer retention. In Bing Liu, Myra Spiliopoulou, Jaideep Srivastava, and Alex Tuzhilin, editors, Proceedings of the 2005 International Workshop on Customer Relationship Management: Data Mining Meets Marketing, November 18--19, 2005, New York City, USA, 2005. [ preprint (PDF) ] With customer relationship management (CRM) companies move away from a mainly product-centered view to a customer-centered view. Resulting from this change, the effective management of how to keep contact with customers throughout different channels is one of the key success factors in today's business world. Company Web sites have evolved in many industries into an extremely important channel through which customers can be attracted and retained. To analyze and optimize this channel, accurate models of how customers browse through the Web site and what information within the site they repeatedly view are crucial. Typically, data mining techniques are used for this purpose. However, there already exist numerous models developed in marketing research for traditional channels which could also prove valuable to understanding this new channel. In this paper we propose the application of an extension of the Logarithmic Series Distribution (LSD) model repeat-usage of Web-based information and thus to analyze and optimize a Web Site's capability to support one goal of CRM, to retain customers. As an example, we use the university's blended learning web portal with over a thousand learning resources to demonstrate how the model can be used to evaluate and improve the Web site's effectiveness.
[8]	Christoph Breidert, Michael Hahsler, and Lars Schmidt-Thieme. Reservation price estimation by adaptive conjoint analysis. In Claus Weihs and Wolfgang Gaul, editors, Classification - the Ubiquitous Challenge, Studies in Classification, Data Analysis, and Knowledge Organization, pages 577--584. Springer-Verlag, 2005. [ preprint (PDF) | at the publisher ] Though reservation prices are needed for many business decision processes, e.g., pricing new products, it often turns out to be difficult to measure them. Many researchers reuse conjoint analysis data with price as an attribute for this task (e.g., Kohli and Mahajan (1991)). In this setting the information if a consumer buys a product at all is not elicited which makes reservation price estimation impossible. We propose an additional interview scene at the end of the adaptive conjoint analysis (Johnson (1987)) to estimate reservation prices for all product configurations. This will be achieved by the usage of product stimuli as well as price scales that are adapted for each proband to reflect individual choice behavior. We present preliminary results from an ongoing large-sample conjoint interview of customers of a major mobile phone retailer in Germany.
[9]	Edward Bernroider, Michael Hahsler, Stefan Koch, and Volker Stix. Data Envelopment Analysis zur Unterstützung der Auswahl und Einführung von ERP-Systemen. In Andreas Geyer-Schulz and Alfred Taudes, editors, Informationswirtschaft: Ein Sektor mit Zukunft, Symposium 4.--5. September 2003, Wien, Österreich, Lecture Notes in Informatics (LNI) P-33, pages 11--26. Gesellschaft für Informatik, 2003. [ at the publisher ] Immer mehr Unternehmen setzen betriebswirtschaftliche Standardsoftwarepakete wie beispielsweise SAP R/3 oder BaaN ein. Die Auswahl und die Einführung solcher Systeme stellt für die meisten Unternehmen ein strategisch wichtiges IT-Projekt dar, das mit massiven Risiken verbunden ist. Bei der Auswahl des am besten geeigneten Systems gilt es einen Gruppenentscheidungsprozess zu unterstützen. Das darauf folgende Einführungsprojekt muss effizient, den ”best practices” entsprechend, durchgeführt werden. In dieser Arbeit wird anhand von Beispielen aufgezeigt, wie beide Prozesse - die Auswahl und die Einführung - durch die Data Envelopment Analysis unterstützt werden können.
[10]	Wolfgang Gaul, Andreas Geyer-Schulz, Michael Hahsler, and Lars Schmidt-Thieme. eMarketing mittels Recommendersystemen. Marketing ZFP, 24:47--55, 2002. [ at the publisher ] Recommendersysteme liefern einen wichtigen Beitrag für die Ausgestaltung von eMarketing Aktivitäten. Ausgehend von einer Diskussion von Input/Output Charakteristika zur Beschreibung solcher Systeme, die bereits eine geeignete Unterscheidung praxisrelevanter Erscheinungsformen erlauben, wird motiviert, warum eine solche Charakterisierung durch die Einbeziehung methodischer Aspekte aus der Marketing Forschung angereichert werden muss. Ein auf der Theorie des Wiederkaufverhaltens basierendes Recommendersystem sowie ein System, das Empfehlungen mittels Analyse des Navigationsverhaltens von Site Besuchern erzeugt, werden vorgestellt. Am Beispiel der Amazon Site werden die Marketing Möglichkeiten von Recommendersystemen verdeutlicht. Abschließend wird zur Abrundung auf weitere Literatur mit Recommendersystem Bezug eingegangen. In einem Ausblick werden Hinweise gegeben, in welche Richtungen Weiterentwicklungen geplant sind.
[11]	Michael Hahsler and Bernd Simon. User-centered navigation re-design for web-based information systems. In H. Michael Chung, editor, Proceedings of the Sixth Americas Conference on Information Systems (AMCIS 2000), pages 192--198, Long Beach, CA, 2000. Association for Information Systems. [ preprint (PDF) | at the publisher ] Navigation design for web-based information systems (e.g. e-commerce sites, intranet solutions) that ignores user-participation reduces the system's value and can even lead to system failure. In this paper we introduce a user-centered, explorative approach to re-designing navigation structures of web-based information systems, and describe how it can be implemented in order to provide flexibility and reduce maintenance costs. We conclude with lessons learned from the navigation re-design project at the Vienna University of Economics and Business Administration.