Search results

Purpose – The purpose of this chapter is to examine the connection between actual variety (the number of stock-keeping units (SKUs)) and amount of useable variety that the consumer perceives. The optimal combination for a retailer is to offer an assortment that maximizes the perceived assortment variety while minimizing the perceived inter-item complexity. Both measures are a function of the actual variety offered in an assortment but other factors such as attribute structure of the individual items, assortment organization, and individual differences can alter the way the actual variety is perceived.Design/methodology/approach – The main methodology used in the chapter is a comprehensive, critical literature review of the empirical research on the topic.Findings – We find that while assortments with a large number of SKUs are desirable for attracting consumers to the category, too large assortments can result in consumer frustration and confusion. On the other hand, when assortments are small, the perceived variety or attention to the category may be limited.Value/originality – Our review shows ways a retailer can adapt to these challenges. First, we show that assortments are viewed in stages. In the first stage, high perceptions of variety are beneficial. When assortments are small, increasing perceived variety can be accomplished by increasing the number of subcategories within the assortment, adding in packaging cues, or using other emotional affective descriptors to further define options within the assortment. In the second or choice stage, too much variety can increase perceived complexity. Perceived complexity at this stage can be reduced by simplifying the complexity of the individual items within the assortment by increasing alignability of attributes, using a simplifying external organizational structure for the assortment, or helping consumers learn their preference.

The choice overload (CO) phenomenon, whereby having many options leads to negative consequences, has been studied widely in psychology and marketing. However, empirical evidence of CO in the tourism context is limited, even though people often encounter numerous choices (e.g., vacation destinations, airfares, hotels, tours) at different stages when planning their holidays. Investigating CO in tourism and hospitality is important because (online) travel advisors are providing tourists with numerous choices, yet they do not know whether or not these decision makers are content after choosing from these large choice sets. This chapter proposes to review and apply insights garnered from the CO literature to tourism research. Accordingly, the chapter proposes five groups of solutions for tourists and travel advisors to avoid CO effects: reducing decision task difficulty, reducing choice-set complexity, reducing preference uncertainty, focusing on decision goals rather than the means to achieve those goals, and adopting appropriate decision-making styles.

Discrete choice models are widely used for estimating the effects of changes in attributes on a given product's likely market share. These models can be applied directly to situations in which the choice set is constant across the market of interest or in which the choice set varies systematically across the market. In both of these applications, the models are used to determine the effects of different attribute levels on market shares among the available alternatives, given predetermined choice sets, or of varying the choice set in a straightforward way.

Discrete choice models can also be used to identify the “optimal” configuration of a product or service in a given market. This can be computationally challenging when preferences vary with respect to the ordering of levels within an attribute as well the strengths of preferences across attributes. However, this type of optimization can be a relatively straightforward extension of the typical discrete choice model application.

In this paper, we describe two applications that use discrete choice methods to provide a more robust metric for use in Total Unduplicated Reach and Frequency (TURF) applications: apparel and food products. Both applications involve products for which there is a high degree of heterogeneity in preferences among consumers.

We further discuss a significant challenge in using TURF — that with multi-attributed products the method can become computationally intractable — and describe a heuristic approach to support food and apparel applications. We conclude with a summary of the challenges in these applications, which are yet to be addressed.

Retail marketing is in the midst of an evolution. The paradigm is shifting from a product-centric to a consumer-centric focus, with a particular emphasis on understanding how consumers transition from harboring an interest in a product to actually purchasing that product. In response, shopper marketing, and in-store marketing (ISM) in particular, have emerged as important mechanisms to influence shopper behavior in brick & mortar and online retail environments. The academic literature is replete with work on what factors of ISM influence shopper behavior. In this chapter, we categorize prominent streams of findings on ISM into firm, customer, competitor and product characteristics of ISM and examine how the notion of a “store” is evolving from bricks to clicks – namely from physical formats to online shopping experiences. Insights from this chapter will help retailers and store managers identify what their customers respond to within a physical store, how technology is changing the way they can capture information on customers, and how shopper behavior is evolving in response to brick & mortar and online retail environments.

After pioneering, but insular, work on the conceptualization and measurement of customer value in business markets undertaken in the 80s and 90s, interest in this topic is substantial since the beginning of this decade. Despite this recent interest, marketing scholars concur that value in business markets is still an under-researched subject. This contribution to the debate is threefold. The paper first proposes an own model of customer value conceptualization in business markets; based on several rounds of testing this theoretically grounded model in managerial practice indications exist to conclude that this model may offer benefits over current models.

Secondly, the paper provides a comprehensive survey of pricing approaches in industrial markets. The paper integrates this literature overview with own empirical findings. Concurrently the paper summarizes extant research on the link between pricing approach and profitability in industrial markets. The paper thirdly proposes a framework for value delivery and value-based pricing strategies in industrial markets. Proposing such a framework is both useful as well as necessary. Useful, since this framework guides new product development and pricing decisions and assists in the implementation of price-repositioning strategies for existing products; necessary, since the theoretical and practical adoption of value-based delivery and pricing strategies may have suffered from the lack of a unifying conceptual framework. Two case studies, one involving the pricing decision for a major product launch at a global chemical company, the other involving value delivery at an industrial equipment manufacturer, illustrate the practical applicability of the proposed framework.

Determining the right number of customers inside a store (i.e. human or customer density) plays a crucial role in retail management strategies. On the one hand, retailers want to maximize the number of visitors they attract in order to optimize returns and profits. On the other hand, ensuring a pleasurable, efficient and COVID-19-proof shopping experience, would go against an excessive concentration of shoppers. Fulfilling both retailer and consumer perspectives requires a delicate balance to be struck. This chapter aims at supporting retailers in making informed decisions, by clarifying the extent to which store layouts influence (perceived) consumer density. Specifically, the chapter illustrates how new technologies and methodologies (i.e. agent-based simulation) can help in predicting a store layout's ability to reduce consumers' perceived in-store spatial density and related perceptions of human crowding, while also ensuring a certain retailers' profitability.

In the context of increasing competition between chained retailers and family-owned retailers, it is key to understand the customer's format choice. Using a logistics regression (i.e., binary logit) model, we explain customers' preference to buy in supermarkets or in small-scale, mom-and-pop stores like nanostores. We collect a representative sample of over 110 surveys from customers in the district of Surco, Lima, Perú, which is a representative area of the features of Lima's residents. We asked customers to focus on analyzing their preference between two retail formats: modern channel (i.e., big-box retailers, supermarkets, and hypermarkets) and traditional channel (i.e., mom-and-pop stores, nanostores). Our surveys included factors pertaining retail format attributes as well as factors related to the purchasing process. The results showed that time available for purchase and a comparatively better perceived service at a mom-and-pop store (i.e., nanostore) are significant factors that explain a higher probability of selecting these retailers, while a better store's ambience benefits more supermarkets. The overall discrete choice model is able to explain 65% of the variance using pseudo R-squared of the actual format choice decisions.

We introduce and analyze an innovative transportation system called flexible mobility on demand (FMOD). FMOD provides a menu of optimized travel options in real-time. Practical considerations related to the business model for FMOD are taken into account as a pre-study for the pilot that will be conducted in Japan.

A modeling framework of FMOD is developed that integrates scheduling, routing, assortment optimization, and choice modeling methodologies. An assortment optimization model is developed with an objective function to maximize operator profit and consumer surplus.

The FMOD system is analyzed through simulation experiments in a Japanese case study. Simulations are presented for Hino city in Tokyo with different numbers of vehicles in the fleet. This analysis provides insights about the fleet size necessary to maintain reasonable levels of operator profit and consumer surplus.

We consider a business model for FMOD that offers flexibility to the operator in terms of who provides resources. The resources are managed with dedicated and non-dedicated services. The experiment indicates that operators can determine the size of the dedicated fleet based on an objective function that maximizes operator profit and passenger satisfaction.