Book Description

Marketing literature and practitioners are in agreement that it is essential for brands in competitive markets to identify segments that should be targeted and to build rational product strategies that target these segments. It is essential because most markets include consumers with heterogeneous preferences and precise segmentation and targeting creates product differentiation, which prevents direct competition and allow the market to reach an optimal profit optimization equilibrium. In this consumer markets' era, defined by practitioners as the big data era, consumers' individual transactions and actions, which reveal their preferences, became highly available to marketers. This allows marketers to greatly improve their targeting and to optimize their profits through that. This dissertation contains three essays that examine optimal products strategies with consideration of individual distributions. Through the models that are built and estimated, individual preferences are identified. Following that, individuals are aggregated into clustered segments, and clear optimization strategy is designed. All the essays build and discuss structural models and estimation strategies. Each estimation uses unique datasets that were selected and organized carefully for the purpose of robust identification of the varied effects that are examined and analyzed. Each essay identifies and considers the individual distributions in the analyses. Altogether, the essays provide a deeper understanding of how to consider individual distributions in varied settings and marketing needs that marketers face frequently. Chapter 1 examines the theory of trying, forgetting, and sales in empirical settings. This is an important model as there are many markets where consumers need to try products for realizing their fit, however after trying, some consumers may forget the fit over time through learning processes of competitive information and other processes. The theory shows that the trying and forgetting model predicts that sales will occur periodically according to the magnitudes of the effects as the sales are used by the brands as product-fit reminders to the targeted consumers. For the empirical examination of this theory, a model that includes trying and forgetting effects within the standard demand side model is built and estimated. The model allows consumers to have heterogeneous tastes and includes treatment for possible endogeneity. Using the demand estimation and including an individual level distribution estimation, the population is divided into segments. Consumers are divided by their utilities for products as it is optimal for firms to target with the regular price the segments that favor the and when they launch sales, they may target more segments as the trying experience may affect their utility and make them be included in the main segment that this firm targets. This segmentation of the data makes it possible to find the equilibrium in which each firm optimizes profits and the market does not enter a situation of direct competition and a Bertrand game as the firms focus on the segments that favor them and launch temporal sales to introduce or remind consumers of the products fit. This allows the identification prices strategies that optimize profits. Chapter 1 also builds a novel dynamic game supply side model together with simulation strategy and technique for that. This is a major contribution as it finds the equilibrium of a multi agent, segments, states, and periods dynamic game for these common settings where firms need to design a long-term, per period, pricing menu as they cannot change their product pricing often. The results of the estimation and simulation show that the trying and forgetting effects are highly significant on the demand side, but are not used well by some brands through their introduction period and afterward, which greatly and negatively influence their market share and long-term profits. Chapter 2 examines a method of finding individual level preference for attributes across products and the importance that it can have on policy makers, marketers, and consumers. It specifically discusses the case of reducing overweight in the population through finding the willingness to pay for the fat attribute of products among consumers that consistently buy fattier products at varied categories and introducing these consumers to products that are healthier for them through promotions on those products. This is an important question as overweight is was recognized as a global epidemic and thus researchers and policy makers are consistently looking for solutions with no consistent finding yet as neither macro taxes of attributes such as sugar or fat nor or macro subsidies of healthier products were feasible, effective, or efficient. It shows that the standard model does not allow targeted and effective promotions to these consumers as there is a gap in willingness to pay for fat through the population compared to the targeted group. However, using the estimation of the individual level distributions, this part shows that it is possible to convert this segment of consumers to choose healthier products through small magnitude promotional pricing. Chapter 3 examines a case that is opposite to the previous chapters. While in the previous chapters the segments were revealed through the estimation and individual distribution estimation methods. The data in this chapter saliently reveals that 20\% of consumers increased their per unit spend in a durable goods category at the first months of the US sub-prime recession of 2008. This hints that a large portion of the consumers became price loving at the beginning of one of the most difficult periods of the US economy. This is clearly the opposite to the expectation, thus chapter examines the data carefully and suggest varied models. Finally, it shows that in this case, a well specified demand model can identify the reasons for the initial confusion coming from the data. Altogether, the essays examine frequent market settings that were not examined before and provide models together with estimation strategies and methods, which allow better optimization of product strategies through the consideration of individual level distributions and through segmenting the population accordingly.