In this thesis, we develop a SKU level market basket model and apply the model to investigate cross-category reference price effects. This research extends previous work on the category-level multivariate logit model (Russell and Petersen 2000). Our model is a generalization of the multivariate logit model which allows for both complementarity and substitution effects at the brand level. The modeling effort in this thesis allows us to use conditional probability distributions of individual items to construct the final joint-distribution of all possible basket selections. The resulting model is very flexible and accommodates a large variety of market structure patterns. The model structure implies that the changes in brand-level marketing variables directly affect category incidence (by altering category attractiveness) and indirectly determine market basket composition. Because the model can be written in a closed form manner, we can easily study the pattern of brand price competition by computing a matrix of cross-price elasticities. We use scanner panel data for the yogurt category to demonstrate the structural flexibility of the model. The results from this application reveal asymmetric competition consistent with price-tier competition literature. We use this model to investigate how consumers' responses to reference prices within a category spillover into their choices across multiple categories. The notion is that a consumer's subjective judgment of the fairness of the price levels in one category influences the choice decisions of related items in other categories. We begin with building within-category SKU-level model based on previous findings from single category reference price models (i.e., internal versus external reference prices, asymmetric response due to loss aversion, and heterogeneity in response across consumers). We then develop four alternative model specifications for cross-category spillover effects and test competing theories about those effects. Using scanner panel data for detergent and softener categories, we discover valuable implications for reference price effects. First, SKU-level reference price effects exist and improve forecasting ability. Second, those reference price effects influence category attractiveness, but do no spillover across categories. Finally, category-level reference dependent evaluation may exist but not be important in forecasting.

In der akademischen Fachliteratur existieren kaum Forschungsergebnisse zu One-to-One-Marketing, die auf Anwendungen im Einzelhandel ausgerichtet sind. Zu den Hauptgründen zählen, dass Ansätze nicht auf die Größe typischer Einzelhandelsanwendungen skalieren und dass die Datenverfügbarkeit auf Händler und Marketing-Systemanbieter beschränkt ist. Die vorliegende Dissertation entwickelt neue deskriptive, prädiktive und präskriptive Modelle für automatisiertes Target Marketing, die auf Representation Learning und Deep Learning basieren, und untersucht deren Wirksamkeit in Praxisanwendungen. Im ersten Schritt zeigt die Arbeit, dass Representation Learning in der Lage ist, skalierbar Marktstrukturen zu analysieren. Der vorgeschlagene Ansatz zur Visualisierung von Marktstrukturen ist vollständig automatisiert und existierenden Methoden überlegen. Die Arbeit entwickelt anschließend ein skalierbares, nichtparametrisches Modell, das Produktwahl auf Konsumentenebene für alle Produkte im Sortiment großer Einzelhändler vorhersagt. Das Deep Neural Network übertrifft die Vorhersagekraft existierender Benchmarks und auf Basis des Modells abgeleitete Coupons erzielen signifikant höhere Umsatzsteigerungen. Die Dissertation untersucht abschließend eine Coupon-Engine, die auf den entwickelten Modellen basiert. Der Vergleich personalisierter Werbeaktionen mit Massenmarketing belegt, dass One-to-One Marketing Einlösungsraten, Umsätze und Gewinne steigern kann. Eine Analyse der Kundenreaktionen auf personalisierte Coupons im Rahmen eines Kundenbindungsprogrammes zeigt, dass personalisiertes Marketing Systemnutzung erhöht. Dies illustriert, wie Target Marketing und Kundenbindungsprogramme effizient kombiniert werden können. Die vorliegende Dissertation ist somit sowohl für Forscher als auch für Praktiker relevant. Neben leistungsfähigeren Modellansätzen bietet diese Arbeit relevante Implikationen für effizientes Promotion-Management und One-to-One-Marketing im Einzelhandel. / Research on one-to-one marketing with a focus on retailing is scarce in academic literature. The two main reasons are that the target marketing approaches proposed by researchers do not scale to the size of typical retail applications and that data regarding one-to-one marketing remain locked within retailers and marketing solution providers. This dissertation develops new descriptive, predictive, and prescriptive marketing models for automated target marketing that are based on representation learning and deep learning and studies the models’ impact in real-life applications. First, this thesis shows that representation learning is capable of analyzing market structures at scale. The proposed approach to visualizing market structures is fully automated and superior to existing mapping methods that are based on the same input data. The thesis then proposes a scalable, nonparametric model that predicts product choice for the entire assortment of a large retailer. The deep neural network outperforms benchmark methods for predicting customer purchases. Coupon policies based on the proposed model lead to substantially higher revenue lifts than policies based on the benchmark models. The remainder of the thesis studies a real-time offer engine that is based on the proposed models. The comparison of personalized promotions to non-targeted promotions shows that one-to-one marketing increases redemption rates, revenues, and profits. A study of customer responses to personalized price promotions within the retailer’s loyalty program reveals that personalized marketing also increases loyalty program usage. This illustrates how targeted price promotions can be integrated smoothly into loyalty programs. In summary, this thesis is highly relevant for both researchers and practitioners. The new deep learning models facilitate more scalable and efficient one-to-one marketing. In addition, this research offers pertinent implications for promotion management and one-to-one marketing.