Search results

This study examines the empirical relationship between four broad antecedents of brand equity (branding strategy, brand structure, brand positioning and target market) and two separate dimensions of revenue premium: price premium and volume premium. Our modeling framework aims to explain how different antecedents of brand equity influence the realized velocity and margin of branded product sales, key drivers of operating cash flow. Our generalizable empirical analyses are based on a representative dataset of over 6,500 brands, across 200 consumer-packaged goods categories, spanning three years. We find that only 20% of brands command revenue premiums, for which volume premiums are the critical determinant. Branding strategies and brand structure primarily impact volume premium. In contrast, brand positioning has little effect. Target market substantially affects both premiums. Overall, these four elements account for 73% and 69% of the explained variations in price and volume premiums, respectively. This study provides generalizable, important, and novel insights for the theory and practice of brand management regarding price positioning and extending brands into new categories.

In the past decade internal communication began to take on a new identity as it supports the many change efforts underway in organizations today. Change communication – how internal messaging effects individual behavior change – is a key element for an organization undergoing transformation. Although research points to the need to communicate during change, very little information is available on what the outcomes are of an internal communication strategy that can positively influence individual behavior change during transformation. This chapter enhances current knowledge on this topic by investigating the relationship of awareness and understanding of change messages to individual behavior change through the case study examination of the intentional organizational transformation experienced in a large, consumer packaged goods (CPG) company.

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.

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.

In the age of “big data,” one of the most important capabilities that differentiates the winners from the losers in the intensely competitive grocery market is how successfully a firm can harness its vast amounts of shopper data to become more shopper-centric. Grocery retailers struggle with how to manage the tremendous amount of data available to them and best leverage their frequent shopper data to derive insights. These data also present an opportunity for academic research on decision-making and evaluation of strategic initiatives. This chapter discusses three case studies that illustrate the various capabilities of frequent shopper data in generating shopper insights. Specifically, using frequent shopper data for millions of shoppers, the three case studies demonstrate how frequent shopper data can be used as an important information asset for understanding differences and similarities among different shopper groups (Case Study 1), as a means to assess the effectiveness of store redesigns/environment changes (Case Study 2), and as a key tool for evaluating program success (Case Study 3). The chapter concludes with a discussion of how successful collaboration between practitioners and academics can be a boon to both business success and academic research.