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Modern day store brands (SB) or private labels (PL), now also popularly called private brands, are brands generally owned and marketed by retailers. They have been active on the market for about 70 years. Over this time span, these brands have evolved from generic, cheap, low-quality economy or budget private labels to lower-priced-than-national brand but acceptable-quality value or standard private labels. Over time, retailers extended the value proposition to the consumer segment seeking higher quality by offering premium private labels. This strategy, called the tiered-private label, comprises offering economy PL to the price-sensitive but not quality sensitive consumers, standard PL to mainstream consumers seeking acceptable quality at lower prices, and premium PL to the quality-sensitive segment seeking value. Over the last 40 years (1980–2020), these versions of private labels have witnessed substantial growth around the world, though the growth is said to be tapering in recent times. As retailers chart the future strategy for their private labels in 2020 and beyond, a pertinent question they face is: Should they continue to offer value or even tiered PL with the same formula that brought them success in the past, or should they morph and adopt new strategies in keeping with current market trends? We support adopting a new strategy that we call the smart PL strategy. The value PL strategy and its manifestation as the tiered PL strategy cater to different consumer segments but focus primarily on price and quality as attributes of choice. In the current marketplace, consumers care not only about price and quality, but also about sustainability, ethics, social responsibility, image, so forth, perhaps more so than earlier generations. They are also more tech-savvy in using digital tools for search and purchase. Retailers, on their part, are now endowed with rich, extensive data that they can tap into to understand customers’ diverse needs, and they are able to harness technology for developing the right product and communication. Thus, the smart PL strategy is a strategy by which retailers can leverage data and technology to market private labels that meet diverse customer needs and achieve greater retail differentiation, store loyalty, margins, and profits. This thought piece provides a road map for developing such a smart PL strategy and directions for future research.

Purpose While prior research has investigated factors that predict consumers’ information search behaviors as they relate to automobiles, such studies were conducted prior to the COVID-19 pandemic. Given that the pandemic has necessitated lockdowns, social distancing, business closures and other disruptions to normal shopping activities, consumer information search behaviors have also been substantially altered as the psychological distance between consumers and marketers has increased. Thus, this study aims to examine these changes and identify patterns of search behavior for a major durable product: automobiles. Design/methodology/approach Using survey data from before and during the pandemic, the study implements Finite Mixture Modeling to unveil latent segments of U.S. consumers’ search behaviors and choices for Japanese automobiles. This analytic method enables capturing consumer unobserved heterogeneity through mixing probabilities guided by individual characteristics. These segments are determined based on consumers’ information search for online and offline marketer-controlled and nonmarketer-controlled sources. Findings The study identifies that two segments of consumers emerge both prior to the pandemic and during the pandemic. These empirically validated findings indicate that the pandemic has led to shifts in consumers’ information search behaviors for Japanese automobiles by relying more on nonmarketer-controlled sources of information. Originality/value This work is among the first comprehensive empirical analyses of consumer search for a major durable product by comparing pre- and during pandemic patterns. Using analytics and econometrics, the first-hand analysis findings offer meaningful implications for marketers and product managers in the automotive industry.

Purpose This paper aims to investigate textual characteristics of customer reviews that motivate companies to respond (sentiment negativity and sentiment deviation) and how aspects of these company responses (response intensity, length and tailoring) affect subsequent customer review quality (comprehensiveness and readability) over time. Design/methodology/approach Leveraging a large data set from a leading app website (Shopify), the authors combine text mining, natural language processing (NLP) and big data analysis to examine the antecedents and outcomes of online company responses to reviews. Findings This study finds that companies are more likely to respond to reviews with more negative sentiment and higher sentiment deviation scores. Furthermore, while longer company responses improve review comprehensiveness over time, they do not have a significant influence on review readability; meanwhile, more tailored company responses improve readability but not comprehensiveness over time. In addition, the intensity (volume) of company responses does not affect subsequent review quality in either comprehensiveness or readability. Originality/value This paper expands on the understanding of online company responses within the digital marketplace – specifically, apps – and provides a new and broader perspective on the motivations and effects of online company responses to customer reviews. The study also extends beyond the short-term focus of prior works and adds to literature on long-term effects of online company responses to subsequent reviews. The findings provide valuable insights for companies (especially those with apps) to enhance their online communication strategies and customer engagement.

Spurred on by the transition to omnichannel retailing and advances in technology, retail delivery process has seen many innovations in recent years. The delivery process, broadly defined, is the set of tasks needed to deliver the product from the retailer to the final consumer. Innovations pertain to modes of delivery, locations of delivery, and trade-offs between delivery speed and delivery charges. We attempt to build a typology of innovations and their use, and summarize their potential costs and benefits to retailers and consumers. It is easily seen that many of the innovations can be labor saving for retailers. But there has been little evidence on consumer reactions. For this purpose, we conduct a national survey to examine the likelihood of adoption of a number of innovations in delivery. We find that although overall interest in these innovations is not high at this early stage, there is a significantly large segment of customers who are more likely to adopt these innovations. These customers are predominantly millennials, have higher incomes, and they are tech-savvy, innovative, environmentally conscious, and value quality. The findings suggest that retailers need to be strategic about choosing targets for successfully propagating these innovations.

Purpose The purpose of this paper is to demonstrate how consumers choose among three different options offered by a firm in a monopolistic setting, namely, to buy a standard product with a non-customizable design, to ask the firm to customize a product using the consumer’s ideal design or to do the entire design task by themselves. The authors also investigate how social preference intensity and the possibility of reselling a product influence a consumer’s decision. Design/methodology/approach The authors develop an analytical (game theoretical) consumer choice framework and incorporate a psychological factor into the model. The authors also empirically validate the analytical findings using simulations. Findings The authors find that as social preference intensity increases, the number of co-producers can either decrease or increase. The authors offer a closed-form solution and interval graphs showing that when the setup price is large (small), the proportion of the market that chooses to do-it-yourself (DIY) is large (small) and an increase in social preference intensity leads to a decrease (increase) in co-production. Originality/value This is the first paper to incorporate a social factor into an economic model in a consumer behavior setting. It is also the first paper to explain how customers’ preferences among possible options, such as DIY (without the firm’s help), co-production (with the firm’s help) and a standard product might change while considering other people’s preferences, as well as given associated costs.

In this research, we address an important gap in the literature as to the search behavior of new car buyers. While the effect of the Internet on this process is known, the literature still lacks a comprehensive study which (1) concurrently covers time periods before and after the launch of the Internet, and (2) compares trends of consumer search across those combined years. Our unique survey dataset, which spans 22 years and includes consumer search information for new cars from both the pre- and post-Internet eras, enables us to investigate this important gap. Using a latent class model, we classify respondents according to variables that measure consumer search for new automobiles. We unveil changes in characteristics of the six latent segments of car shoppers. Our main findings show that, over the years since the advent of the Internet, the segment of car buyers who mainly search through car dealers/stores has been shrinking drastically. We also find evidence that, over time, the heavy Internet user segment has become less likely to have decided on the manufacturer/dealer prior to searching. Our findings benefit researchers, practitioners, car manufacturers, dealers, and buyers.

When judging the expensiveness of a product or service, consumers often make comparisons to similar offerings that serve as reference points. Extant pricing literature shows that reference items in the consideration set may trigger a \"contrast effect,\" where higher-priced items make the target item seem less expensive. Two studies show that the effect of reference price depends on the consumer's level of abstract thinking-or \"construal level\" -at the time of judgment. Concrete construal leads to the standard contrast effect, but abstract construal leads to an assimilation effect, where higher-priced reference items make the target seem more expensive.

•We investigate multichannel assortment planning decisions across retailers.•Retailer’s inventory policy and distribution strategy through an analytical model.•Test relationship between product attributes & retailers’ channel choice.•Retailers are less likely to drop-ship colored/irregularly-sized products.•Detect nonlinearities and thresholds in the effects or product value. [Display omitted] Retailers that sell seasonal products face significant challenges when planning inventory assortment. The incorporation of drop-shipping into their operations, wherein suppliers own and ship products directly to consumers at retailers’ requests, has only complicated these challenges. This study investigates multichannel assortment planning of retailers that sell seasonal products. We first capture structural properties of multichannel retailing of seasonal products through a simple and parsimonious analytical model. The analytical model uncovers key seasonal product attributes that make it more attractive for retailers to allocate a product for sale in the drop-shipping channel than in the store channel. We then empirically assess the findings of the analytical model. Using a rich and unique dataset from the fashion retail industry, we test relationships between product attributes and retailers’ channel choice. The application of a generalized linear latent and mixed model controls for selection bias by jointly estimating retailers’ likelihood of allocating a product’s inventory to the drop-shipping channel and the allocated volume in each channel according to the product’s characteristics. The empirical findings suggest that retailers are less likely to drop-ship products that are colored, irregularly sized, and offered in more style variants. They also unveil cross-channel effects in terms of inventory amounts allocated for sale in each channel according to those characteristics. Our analytical and empirical assessments jointly demonstrate the complementary roles played by drop-shipping and store channels for seasonal products and offer important academic and practical implications.

With three essays, this dissertation focuses on investigating demand and pricing policies for short life cycle goods, and studies consumer search behavior and choice for automobiles. The former comprises the first two dissertation essays (Chapters 2 and 3) in which I specifically focus on fashion products by using a unique dataset from a well-known U.S. fashion retailer. The final essay (Chapter 4) concentrates on the automobile industry in the US where efficiency of search outcome through various search sources for cars is studied. In the first essay (Chapter 2), using data on women’s coats, I combine revenue management and durable goods literature to develop a demand model which estimates sales at every price markdown level at each time period of the fashion season. The demand model is estimated using the General Method of Moments (GMM). Next, I run simulations to study the demand variations based on different pricing policies. I find price markdowns which are not deep and are implemented early in the season to maximize the retailer’s revenue levels. In the second essay (Chapter 3), I use a Finite Mixture Model (FMM) to find latent classes based on fashion product characteristics for women’s and men’s coats in the US. I develop a dynamic programming model and solve it with the class-specific coefficients of the FMM model. Results provide the retailer with the optimal (1) timing and (2) depth of markdowns given fashion product characteristics at each time period. The optimal markdown policy offers over 3% increases in retailer’s revenue. In the third essay (Chapter 4) I investigate customer search efficiency for automobiles. Having data on car specifications, I use Data Envelopment Analysis (DEA) technique to measure the efficiency of cars based on the value of their attributes per dollar spent (price). Next using survey data on consumer search time and final choice, I use the estimated efficiency parameters to investigate the contribution of each search source in making a car choice. I find customers who search using online and offline third party sources purchase more efficient cars, while males and those who knew the manufacturer prior to purchase buy less efficient cars.