Prof. Klapper erforscht, wie Konsumenten Entscheidungen treffen — insbesondere bei langlebigen Gütern wie Autos und digitalen Produkten. Sein aktueller Fokus liegt auf der Transparenz und Produktwahl in digitalen Umgebungen: Wie beeinflussen verfügbare Informationen, Preisgestaltung und Produktvielfalt die Kaufentscheidung? Er nutzt dazu Daten aus realen Märkten (Automobil, Einzelhandel, Software), kombiniert sie mit experimentellen Methoden und Machine Learning, um Verhaltensmuster und Konsumentenheterogenität zu verstehen. Für Unternehmen liefert er konkrete Erkenntnisse zur Preisoptimierung, Sortimentsgestaltung und Kundenidentifikation — etwa wie Einzelhandelsketten ihre Assortments bewerten oder wie Softwarefirmen ihre Freemium-Modelle verbessern können.
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Prof. Dr. Daniel Klapper
HU-FIS-Profil ↗SFB/TRR 190/2: Transparenz und Produktwahl in digitalen Umgebungen (TP A05)
university
Zeitraum: 02/2013 - 03/2014 Projektleitung: Prof. Dr. Daniel Klapper
Zeitraum: 03/2013 - 12/2013 Projektleitung: Prof. Dr. Daniel Klapper
Förderer: DFG Sachbeihilfe Zeitraum: 09/2013 - 08/2016 Projektleitung: Prof. Dr. Daniel Klapper
The Review of Economics and Statistics · DOI
How a cost shock is passed through to final consumer prices may relate to nominal price stickiness and rigidities, the existence of nonadjustable cost components, strategic markup adjustments, or other contract terms along the supply distribution chain. This paper presents a simple framework to assess the potential role of nonlinear pricing contracts and vertical restraints, such as resale price maintenance or wholesale price discrimination in the supply chain, in explaining the degree of pass-through from upstream cost shocks in the ground coffee category to downstream retail prices. We find that resale price maintenance increases pass-through rate.
Journal of the Academy of Marketing Science · DOI
Journal of Marketing Research · DOI
The authors show how to use microlevel survey data from a tracking study on brand awareness in conjunction with data on sales and advertising expenditures to improve the specification, estimation, and interpretation of aggregate discrete choice models of demand. In a departure from the commonly made full information assumption, they incorporate limited information in the form of choice sets to reflect that consumers may not be aware of all available brands at purchase time. They find that both the estimated brand constants and the price coefficient are biased downward when consumer heterogeneity in choice sets is ignored. These biased estimates can lead firms to make costly price-setting mistakes. In addition, the tracking data enable the authors to identify separately two processes by which advertising influences market shares. They find that advertising has a direct effect on brand awareness (inclusion in choice set) in addition to its effect on consumer preferences (increase in utility). This improved understanding of how advertising works enhances researchers’ ability to make policy recommendations.