Daniel Kaimann

'To Infinity and Beyond!' - A Genre-Specific Film Analysis of Movie Success Mechanisms

The objective of this study is the analysis of movie success mechanisms in a genre-specific context. Instead of the examination of all time box office champions, we focus on the two film genres of computer animated and comic book based films. By introducing the concept of the motion-picture marketing mix, which represents a set of tactical marketing tools in order to strengthen a company’s strategic customer orientation, we are able to systematically identify key movie success factors. We conduct a cross-sectional empirical analysis across regional distinctions based on dataset that covers a time horizon of more than 30 years. We find empirical evidence that actors with ex ante popularity, award nominations and the production budget represent key movie success mechanisms and significantly influence a movie’s commercial appeal. Additionally, word-of-mouth creates reputation effects that also significantly affects box office gross.

Price competition and the Bertrand model: The paradox of the German mobile discount market

ABSTRACT We investigate the degree of price competition among telecommunication firms. Underlying a Bertrand model of price competition, we empirically model pricing behaviour in an oligopoly. We analyse panel data of individual pricing information of mobile phone contracts offered between 2011 and 2017. We provide empirical evidence that price differences as well as reputational effects serve as a signal to buyers and significantly affect market demand. Additionally, we find that brands lead to an increase in demand and thus are able to generate spillover effects even after price increase.

More than skills: A novel matching proposal for multiplayer video games

Abstract Multiplayer video games are high-involvement products with multiplatform and multiplayer characteristics which aim to enhance player retention by optimizing the matching of teams in accordance with their skills and attributes. However, relatively little academic research has been conducted into the ways in which player attributes can be used to optimize the formation of teams in multiplayer video games. Our study addresses this deficiency in the literature by analyzing a dataset from a popular online multiplayer game that includes historic behavioral data of 6.9 million players participating in 862,664 unique game rounds. We analyze the observable factors associated with longer duration of participation in each round, finding that player retention improves in the presence of player-versus-player combat, variety and heterogeneity. We also show that player retention diminishes as a result of the absence of particular role or vehicle use within a given round. Based on the findings of the analysis, we develop a novel approach called nested matching to assign players to teams with an optimal mixture of skills and inherent and complementary attributes.

Pairwise versus Pointwise Ranking: A Case Study

Object ranking is one of the most relevant problems in the realm of preference learning and ranking. It is mostly tackled by means of two different techniques, often referred to as pairwise and pointwise ranking. In this paper, we present a case study in which we systematically compare two representatives of these techniques, a method based on the reduction of ranking to binary classification and so-called expected rank regression (ERR). Our experiments are meant to complement existing studies in this field, especially previous evaluations of ERR. And indeed, our results are not fully in agreement with previous findings and partly support different conclusions.

The impact of customer reviews and advertisement efforts on the performance of experience goods in electronic markets

ABSTRACT When searching products online, costumers are facing a multitude of information signals of product quality they need to process – both separately and jointly – for relevance and reliability. A potentially reliable informations source is past experience of customers expressed in online reviews. In this article, we analyse the impact of customer reviews and additional signals of quality on buying behaviour in electronic markets. To empirically estimate and separate the effects, we use data from the Apple App Store covering 5792 daily observations from 2015. We find clear evidence to suggest that reviews and ads have a significantly positive influence on download rates. We also find empirical evidence to suggest that a considerable degree of interaction is important in explaining variations in market performance, especially between customer average ratings and unanimity of customer reviews.