The paper examines the opportunities in and possibilities arising from Big Data in retailing, particularly along five major data dimensions -data pertaining to customers, products, time, (geo-spatial) location and channel. Much of the increase in data quality and application possibilities comes from a mix of new data sources, a smart application of statistical tools and domain knowledge combined with theoretical insights. The importance of theory in guiding any systematic search for answers to retailing questions, as well as for streamlining analysis remains undiminished, even as the role of Big Data and predictive analytics in retailing is set to rise in importance, aided by newer sources of data and large-scale correlational techniques. The Statistical issues discussed include a particular focus on the relevance and uses of Bayesian analysis techniques (data borrowing, updating, augmentation and hierarchical modeling), predictive analytics using big data and a field experiment, all in a retailing context. Finally, the ethical and privacy issues that may arise from the use of big data in retailing are also highlighted.2
The paradigm shift that the Internet has brought about in communication has opened up a plethora of opportunities for outsourcing business processes (BPO) across continents. Success lessons in manufacturing sub-contracting are found to be relevant for understanding the logic of BPO. Outsourcing involves transferring certain value contributing activities or processes to another firm to save costs and for the principal to focus on its areas of key competence. The possibilities of disaggregating value elements for the purpose of creating value in them at the sub-contractors' premises and final aggregation and synthesis at the parent organization are determined by the nature of industry, limitations of coordination and control, product maturity, and level of inter-firm competition.IT-enabled services (ITES) includes services that can be outsourced using the powers of IT; the extent to which this is possible depends on the industry, location, time, costs, and managerial perception of the risks involved. The Internet has facilitated execution of several activities, previously done within geographical proximity to the firm, from remote low-labour cost locations, drawing both transaction cost and production cost efficiencies.Some of the factors that come in the way of parents setting up their own operations in India and have significant implications for the growth trajectory of Indian BPOs are: direct cost of operations and scale economies long-term assessment of India as a low cost centre cost-benefit assessment of own vs rented possible loss of control over their transactions and confidentiality and security of the data if an outsider handles them brand implications of perceived drop in quality robustness of existing systems and processes. Many BPO firms do not seem to realize the possible exit barriers and strategies to manage exit, if necessary. What happened in the dot com era can very well happen in the BPO space also unless care is taken to manage this rapid growth while retaining productivity and quality.Two key capabilities required for success in ITES space are: capabilities to understand customer needs in the specific domains and acquiring business (BD capabilities) and capabilities to execute them efficiently (Ops capabilities). ITES firms are likely to bifurcate their firm into two parts based on these two critical success factors.The successful segregation of value elements in a number of processes has enabled value configuration in as many ways as required by customers, both in the case of product and service components of customer value. The current trend in outsourcing will go up when such analysis-synthesis becomes a routine. This will be accelerated also because the capabilities required to do so depend not only on technical skills and knowledge in a domain but also strong process capabilities.The trend of outsourcing is likely to continue to grow in the future despite temporary political protests because of the robust arguments outsourcing finds for itself in the economics literature, both in terms o...
W e develop and implement a Bayesian semiparametric model of demand under interproduct competition that enables us to assess the respective contributions of brand-SKU (stock keeping unit) hierarchy and interproduct similarity to explaining and predicting demand. To incorporate brand-SKU hierarchy effects, we use Bayesian hierarchical clustering inherent in a nested Dirichlet process to simultaneously partition brands, and SKUs conditional on brands, into groups of "similarity clusters." We examine cluster memberships and postprocess the Markov chain Monte Carlo output to infer cluster properties by accounting for parameter uncertainty. Our proposed approach lends to a spatial competition interpretation in latent attribute space and helps uncover the extent to which competition across SKUs in the latent attribute space is local or global. In a related vein, we discuss the implications of well-defined groups of similar SKUs as subcategory or submarket boundaries in latent attribute space. We empirically test our model using aggregate beer category sales data from a midsize U.S. retail chain. We find that branding hierarchy effects dominate those from product similarity. We find that the model partitions the 15 brands in the data into 4 brand clusters and the 96 SKUs into 25 SKU clusters conditional on brand cluster membership. In estimating a set of models of spatial interproduct competition, we find that SKU competition is more local than global in that only subsets of products compete within groups of comparable products. Finally, we discuss the substantive implications of our results.
T he existence of reference price effects in consumer decision making is well documented in prior research, but few studies focus on its implications for firms' strategic behavior. Using a competitive model, we address this gap by examining how firms' product positioning and pricing strategies in a non-durable goods market (where consumers repeatedly purchase products from the category) are affected compared with a benchmark situation in which reference price effects are not pertinent. In a model with internal reference price effects, we find that as the salience of reference price effect increases, (a) product differentiation first decreases and then increases; and (b) firm profits first decrease and then increase. Using data from Information Resources, Inc. (IRI) dataset, we empirically validate our findings. We contribute to the product positioning literature by uncovering the role of internal reference price effects on product positioning and profits.
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