Retailing Innovations in a Globalizing Retail Market Environment

In recent years, the combination of economic growth and population growth in emerging markets and less developed markets has accelerated the progression of globalization of retailing and globalization by retailers. The challenges faced by global and globalizing retailers (retailers who currently have or intend to establish a market presence in mature markets, emerging markets and less developed markets) can be more daunting compared to those faced by firms in other industries such as automobiles, steel, and computers. Retailing innovations that are responsive to the characteristics of distinctive national markets and broader aggregations of markets such as mature, emerging and less developed markets are critical to the success of global and globalizing retailers. Against this backdrop, this paper focuses on retailing innovations in the context of a globalizing retailing environment. It attempts to shed insights into the characteristics of retailing innovations conducive to superior performance in distinctive national markets and across broader aggregations of markets. Towards this end, we first examine the environmental conditions of markets in different development stages, namely mature, emerging and less developed markets, and explore consumer based, industry based, and legal/regulatory based challenges faced by globalizing retailers in these markets. Second, we show how these challenges can be transformed into opportunities with retailing innovations. We conclude with a roadmap for future research and present propositions on future development with respect to retailing innovations in these markets.     

How to sell services more profitably

When products become commodities, manufacturing companies may seek to differentiate themselves with value-added services--a potentially profitable strategy. Unfortunately, companies often stumble in the effort. Reinartz and Ulaga conducted in-depth studies of 18 leading companies in a broad variety of product markets to learn what distinguished the successes from the rest. They discovered four steps to developing a profitable services capability. RECOGNIZE THAT YOU ALREADY HAVE A SERVICE COMPANY: You can identify and charge for simple services--as Merck did when it stopped quietly absorbing shipping costs. Switching services from free to fee clarifies their value for managers as well as for customers. INDUSTRIALIZE THE BACK OFFICE: To prevent delivery costs from eating up service-offering margins, build flexible service platforms, closely monitor process costs, and exploit new technologies that enable process innovations. The Swedish bearings manufacturer SKF provided off-site access to an online monitoring tool that could warn of potential failure in customers' machines. CREATE A SERVICE-SAVVY SALES FORCE: Services require longer sales cycles and, often, decisions from high up in a customer's hierarchy; what's more, product salespeople may be inimical to change. Schneider-Electric did a major overhaul of its sales organization and trained its people to switch from cost-plus pricing to value-based pricing. FOCUS ON CUSTOMERS' PROCESSES AND THE OPPORTUNITIES THEY AFFORD FOR NEW SERVICE OFFERINGS: You may need to acquire new capabilities to take advantage of those opportunities: The industrial coatings specialist PPG had to learn how painting robots function after it offered to take over Fiat's Torino paint shop. Services can both lock in customers and help acquire new accounts. They should be developed with care and attention.     

An empirical comparison of the efficacy of covariance-based and variance-based SEM

Variance-based SEM, also known under the term partial least squares (PLS) analysis, is an approach that has gained increasing interest among marketing researchers in recent years. During the last 25 years, more than 30 articles have been published in leading marketing journals that have applied this approach instead of the more traditional alternative of covariance-based SEM (CBSEM). However, although an analysis of these previous publications shows that there seems to be at least an implicit agreement about the factors that should drive the choice between PLS analysis and CBSEM, no research has until now empirically compared the performance of these approaches given a set of different conditions. Our study addresses this open question by conducting a large-scale Monte-Carlo simulation. We show that justifying the choice of PLS due to a lack of assumptions regarding indicator distribution and measurement scale is often inappropriate, as CBSEM proves extremely robust with respect to violations of its underlying distributional assumptions. Additionally, CBSEM clearly outperforms PLS in terms of parameter consistency and is preferable in terms of parameter accuracy as long as the sample size exceeds a certain threshold (250 observations). Nevertheless, PLS analysis should be preferred when the emphasis is on prediction and theory development, as the statistical power of PLS is always larger than or equal to that of CBSEM; already, 100 observations can be sufficient to achieve acceptable levels of statistical power given a certain quality of the measurement model.     

The mismanagement of customer loyalty

Who wouldn't want loyal customers? Surely they should cost less to serve, they'd be willing to pay more than other customers, and they'd actively market your company by word of mouth, right? Maybe not. Careful study of the relationship between customer loyalty and profits plumbed from 16,000 customers in four companies' databases tells a different story. The authors found no evidence to support any of these claims. What they did find was that the link between customers and profitability was more complicated because customers fall into four groups, not two. Simply put: Not all loyal customers are profitable, and not all profitable customers are loyal. Traditional tools for segmenting customers do a poor job of identifying that latter group, causing companies to chase expensively after initially profitable customers who hold little promise of future profits. The authors suggest an alternative approach, based on well-established "event-history modeling" techniques, that more accurately predicts future buying probabilities. Armed with such a tool, marketers can correctly identify which customers belong in which category and market accordingly. The challenge in managing customers who are profitable but disloyal--the "butterflies"--is to milk them for as much as you can while they're buying from you. A softly-softly approach is more appropriate for the profitable customers who are likely to stay loyal--your "true friends." As for highly loyal but not very profitable customers--the "barnacles"--you need to find out if they have the potential to spend more than they currently do. And, of course, for the "strangers"--those who generate no loyalty and no profits--the answer is simple: Identify early and don't invest anything.     

Getting the most out of all your customers

Companies spend billions of dollars on direct marketing, targeting individual customers with ever more accuracy. Yet despite the power of the myriad data-collecting and analytical tools at their disposal, they're still having trouble optimizing their direct-marketing investments. Many marketers try to minimize costs by pursuing only those customers who are cheap to find and cheap to keep. Others try to get the most customers they possibly can and keep all of them for as long as they can. But a customer need not be loyal to be highly profitable, and many loyal customers turn out to be highly unprofitable. Companies can get more out of direct marketing if they see it as a single system for generating profits than if they try to maximize performance measures at each stage of the process. This article describes a tool for doing just that. Called ARPRO (Allocating Resources for Profits), the tool is essentially a complex regression analysis that can estimate the impact of a company's direct-marketing investments on the profitability of its customer pool. With data that companies already gather, the tool can show managers how much to spend on acquisition versus retention and even what percentage of their funds they should allocate to the different direct-marketing channels. Using the model, companies can easily see that even small deviations from the optimal levels of customer profitability are expensive. Applying it to one catalog retailer showed, for instance, that a 10% reduction in marketing costs would lead to a 1.8 million dollar drop in long-term customer profits. Conversely, spending 69% less on marketing would actually increase average customer profitability at one B2B service provider by 42%. What's more, the tool can show that finding the optimal balance between investments in acquisition and retention can be more important than finding the optimum amount to invest overall.     

A retrospective review of the first 35 years of the International Journal of Research in Marketing

The International Journal of Research in Marketing (IJRM) publishes groundbreaking research on a range of topics related to marketing. Academics, scholars, and practitioners value the journal for its original and well-executed content. Using bibliometrics, this study summarizes the journal’s first 35 years in terms of its publication trends, authorship patterns, citation structure, and themes, as well as the clustering of IJRM’s articles published between 1984 and 2018. This study identifies the IJRM’s most influential articles, most prolific contributors and their affiliations, and frequently used keywords and reveals their semantic associations along with factors influencing citations of the IJRM corpus. As the first objective assessment of the journal’s first 35 years, the review also suggests some potential avenues to target future submissions.     

Knowing what to sell, when, and to whom

Despite an abundance of data, most companies do a poor job of predicting the behavior of their customers. In fact, the authors' research suggests that even companies that take the greatest trouble over their predictions about whether a particular customer will buy a particular product are correct only around 55% of the time--a result that hardly justifies the costs of having a CRM system in the first place. Businesses usually conclude from studies like this that it's impossible to use the past to predict the future, so they revert to the timeworn marketing practice of inundating their customers with offers. But as the authors explain, the reason for the poor predictions is not any basic limitation of CRM systems or the predictive power of past behavior, but rather of the mathematical methods that companies use to interpret the data. The authors have developed a new way of predicting customer behavior, based on the work of the Nobel Prize-winning economist Daniel McFadden, that delivers vastly improved results. Indeed, the methodology increases the odds of successfully predicting a specific purchase by a specific customer at a specific time to about 85%, a number that will have a major impact on any company's marketing ROI. What's more, using this methodology, companies can increase revenues while reducing their frequency of customer contact-evidence that overcommunication with customers may actually damage a company's sales.     

The impact of digital transformation on the retailing value chain

Consumers have traditionally made purchase decisions at the store shelf, giving institutional brick-and-mortar retailers great power to learn about and influence behaviors and preferences. With the rise of e-commerce, mobile shopping, and most recently smart technologies, new competitors threaten this long-standing supremacy. Adopting a value-creation perspective, we analyze how digitization started the erosion of institutional retailing as the primary interface to the customer. We develop a framework that identifies five new sources of value creation and propose how these advance and transform competition for this interface. Depending on the importance of the new sources of value creation (in different purchase situations), stationary retailing may prevail as an important interaction point in a multichannel decision journey. However, increasing diffusion of branded-product platforms including connected devices and online retail platforms is shifting this authority to new players. For the parties involved in this multilayered competition, acknowledging the changes and actively managing their position in the evolving eco-systems is crucial.     

Improving customer profit predictions with customer mindset metrics through multiple overimputation

Journal of the Academy of Marketing Science - Research and practice have called for the incorporation of customer mindset metrics (CMMs) to improve the accuracy of models that predict individual...