Implications of probabilistic data modeling for rule mining

Mining association rules is an important technique for discovering meaningful patterns in transaction databases. In the current literature, the properties of algorithms to mine associations are discussed in great detail. In this paper we investigate properties of transaction data sets from a probabilistic point of view. We present a simple probabilistic framework for transaction data and its implementation using the R statistical computing environment. The framework can be used to simulate transaction data when no associations are present. We use such data to explore the ability to filter noise of confidence and lift, two popular interest measures used for rule mining. Based on the framework we develop the measure hyperlift and we compare this new measure to lift using simulated data and a real-world grocery database.Series: Research Report Series / Department of Statistics and Mathematic

A Combined Approach for Segment-Specific Analysis of Market Basket Data

There are two main research traditions for analyzing market basket data that exist more or less independently from each other, namely exploratory and explanatory model types. Exploratory approaches are restricted to the task of discovering cross-category interrelationships and provide marketing managers with only very limited recommendations regarding decision making. The latter type of models mainly focus on estimating the effects of category-level marketing mix variables on purchase incidences assuming cross-category dependencies. We propose a procedure that combines these two modeling approaches in a novel two-stage procedure for analyzing cross-category effects based on shopping basket data: In a data compression step we first derive a set of market basketprototypes and generate segments of households with internally more distinctive(complementary) cross-category interdependencies. Utilizing the information oncategories that are most responsible for prototype construction, segment-specificmultivariate logistic models are estimated in a second step. Based on the data-driven way of basket construction, we can show significant differences in cross-effects and related price elasticities both across segments and compared to the global (segment-unspecific) model

Topic modeling in marketing: recent advances and research opportunities

Using a probabilistic approach for exploring latent patterns in high-dimensional co-occurrence data, topic models offer researchers a flexible and open framework for soft-clustering large data sets. In recent years, there has been a growing interest among marketing scholars and practitioners to adopt topic models in various marketing application domains. However, to this date, there is no comprehensive overview of this rapidly evolving field. By analyzing a set of 61 published papers along with conceptual contributions, we systematically review this highly heterogeneous area of research. In doing so, we characterize extant contributions employing topic models in marketing along the dimensions data structures and retrieval of input data, implementation and extensions of basic topic models, and model performance evaluation. Our findings confirm that there is considerable progress done in various marketing sub-areas. However, there is still scope for promising future research, in particular with respect to integrating multiple, dynamic data sources, including time-varying covariates and the combination of exploratory topic models with powerful predictive marketing models

Hedonic and Utilitarian Shopper Types in Evolved and Created Retail Agglomerations

This paper focuses on the impact of hedonic and utilitarian values of shopping on retail agglomeration patronage issues, in particular on shopping behaviour and the perception of retail agglomerations. Our empirical study is based on a discussion of agglomerations’ potential to attract utilitarian and hedonic shopper types. A sample of 2,139 customers were interviewed in a peripheral shopping mall as well as on an inner city shopping street and confronted with a multi-item scale operationalising shopping values as developed by Babin et al. (1994). Using a standard fuzzy c-means clustering algorithm we identified four distinct shopper types. The results show that hedonists are represented by a higher number of females, earn lower individual incomes and are less educated compared to utilitarians. Interestingly, a higher share of hedonists visited the shopping mall. Overall, they make more shopping trips to agglomerations, stay there longer, visit more stores and – depending on the agglomeration format – spend less than or the same amount as utilitarians. Finally, we see that those customers who are attracted by agglomerations because of atmospheric and price stimuli are typical hedonists

WIE WIRKT MOBILE WERBUNG? EMIRISCHE BEFUNDE AUS EINER SMS-WERBEKAMPAGNE

Der vorliegende Beitrag untersucht Einflussgrößen auf die Akzeptanz und Einstellung von Kunden gegenüber Werbemaßnahmen unter Verwendung mobiler Kommunikationstechnologien. Im Gegensatz zu den bislang vorgestellten, meist kontextunabhängigen Ansätzen wird hier ein Untersuchungsdesgin vorgestellt, welches die Wirkungsmechanismen einer konkreten SMSbasierten Promotions-Aktion auf psychologische Werbezielgrößen näher beleuchtet. Im Rahmen eines kontrollierten Feldexperiments wird das Modell einer empirischen Evaluation unterzogen. Die Ergebnisse belegen die hohe Bedeutung einer zielgruppenadäquaten Auswahl von Produkten für eine Bewerbung durch Direktmarketing-Aktionen. Darüber hinaus zeigt sich einmal mehr, dass das Einverständnis bzw. die Freiwilligkeit der Rezipienten zum Bezug mobiler Werbebotschaften eine wichtige Voraussetzung für die Akzeptanz dieses aufkommenden Werbemediums darstellt

The carrot and the stick in online reviews: determinants of un-/helpfulness voting choices

With increasing volumes of customer reviews, ‘helpfulness’ features have been established by many online platforms as decision-aids for consumers to cope with potential information overload. In this study, we offer a diferentiated perspective on the drivers of review helpfulness. Using a hurdle regression setup for both helpfulness and unhelpfulness voting behavior, we aim to disentangle the differential effects of what drives reviews to receive any votes, how many votes they receive and whether these effects differ for helpful against unhelpful review voting behavior. As potential driving factors we include reviews’ star rating deviations from the average rating (as a proxy for confrmation bias), the level of controversy among reviews and review sentiment (consistency of review content), as well as pricing information in our analysis. Albeit with opposite effect signs, we find that revealed review un-/helpfulness is consistently guided by the tonality (i.e., the sentiment of review texts) and that reviewers tend to be less critical for lower priced products. However, we find only partial support for a confirmation bias with differential effects for the level of controversy on helpfulness versus unhelpfulness review votings. We conclude that the effects of voting disagreement are more complex than previous literature suggests and discuss implications for research and management practice

An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data

Retail managers have been interested in learning about cross-category purchase behavior of their customers for a fairly long time. More recently, the task of inferring cross-category relationship patterns among retail assortments is gaining attraction due to its promotional potential within recommender systems used in online environments. Collaborative filtering algorithms are frequently used in such settings for the prediction of choices, preferences and/or ratings of online users. This paper investigates the suitability of such methods for situations when only binary pick-any customer information (i.e., choice/nonchoice of items, such as shopping basket data) is available. We present an extension of collaborative filtering algorithms for such data situations and apply it to a real-world retail transaction dataset. The new method is benchmarked against more conventional algorithms and can be shown to deliver superior results in terms of predictive accuracy. (author's abstract)Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science

How Can we Derive Consensus Among Various Rankings of Marketing Journals?

The identification of high quality journals often serves as a basis for the assessment of research contributions. In this context rankings have become an increasingly popular vehicle to decide upon incentives for researchers, promotions, tenure or even library budgets. These rankings are typically based on the judgments of peers or domain experts or scientometric methods (e.g., citation frequencies, acceptance rates). Depending on which (combination) of these ranking approaches is followed, the outcome leads to more or less diverging results. This paper addresses the issue on how to construct suitable aggregate (subsets) of these rankings. We present an optimization based consensus ranking approach and apply the proposed method to a subset of marketing-related journals from the Harzing Journal Quality List. Our results show that even though journals are not uniformly ranked it is possible to derive a consensus ranking with considerably high agreement among the individual rankings. In addition, we explore regional differences in consensus rankings.Series: Research Report Series / Department of Statistics and Mathematic

Segmentation based competitive analysis with MULTICLUS and topology preserving networks

Two neural network approaches, Kohonen's Self-Organizing (Feature) Map (SOM) and the Topology Representing Network (TRN) of Martinetz and Schulten are employed in the context of competitive market structuring and segmentation analysis. In an empirical study using brands preferences derived from household panel data, we compare the SOM and TRN approach to MULTICLUS, a parametric approach which also simultaneously solves the market structuring and segmentation problem. Our empirical analysis shows several benefits and shortcomings of the three methodologies under investigation, MULTICLUS, SOM, and TRN. As compared to MULTICLUS, we find that the non-parametric neural network approaches show a higher robustness against any kind of data preprocessing and a higher stability of partitioning results. As compared to SOM, we find advantages of TRN which uses a more flexible concept of adjacency structure. In TRN, no rigid grid of units must be specified. A further advantage of TRN lies in the possibility to exploit the information of the neighborhood graph which supports ex-post decisions about the segment configuration at both the micro and the macro level. However, SOM and TRN also have some drawbacks as compared to MULTICLUS. The network approaches are, for instance, not accessible to inferential statistics. Our empirical study indicates that especially TRN may represent a useful expansion of the marketing analysts tool box. (author's abstract)Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science

A Combined Approach for Segment-Specific Analysis of Market Basket Data ∗

This research was supported by the Deutsch