Arindam Banerjee

Store choice behaviour in an evolving market

The objective of this study is to identify, at a macro level, the drivers of store choice in various product categories, in the context of the evolving retail industry in India. The paper attempts to correlate the distinct store features as perceived by respondents with the true motivations of various consumers in patronising various stores. In the process it provides insight as to whether the average Indian consumer values the new store dimensions offered by retailers as a part of the new formats emerging in the market place. The framework evolved for evaluating effectiveness of newer store formats is necessary since it has a major impact on the overall profitability of the retailing business. Suggests that customers in a developing market such as India do not require the service paraphernalia offered by many of the new store formats emerging in the market and notes that this may cast a serious doubt over the retail revolution, which has taken shape in the Indian markets lately. Some hypotheses about the evolution of the retailing business in India, which requires further investigation, are suggested.

Deciding Where to Buy: Store Choice Behaviour of Indian Shoppers

Store choice is a decision that a shopper is fairly involved in. It is important for a store to understand this behaviour for developing marketing strategies to attract and keep its clientele. It is found that shoppers choose the store based on many aspects that could be classified as primary and image based. It is also found that the importance of each of these aspects changes with the kind of store the shopper wants to visit. In the Indian context where the shopper does not have much variety in store format, the type of store is recognized by the kind of product the store deals in. The paper is an attempt to understand this behaviour of the shopper. The shoppers are explored for the primary reasons for choosing a store. Then, using a factor analysis, the several image dimensions are classified. Further, using multinomial logit regression, the store choice pattern is studied across different types of store. Implications for the managers in the retail business are drawn and future research directions have been highlighted.

International service outsourcing: Using offshore analytics to identify determinants of value‐added outsourcing

Purpose – International outsourcing has been traditionally looked upon as a low end cost effective servicing option to take advantage of the cost arbitrage that exists across countries. Of late, many outsourcing vendors have realized that the advantages of cost differentials that spurred a lot of the global outsourcing business in the past 20 years will disappear in the medium term. The purpose of this paper is to provide a perspective about how much value addition, besides cost, traditional outsourcing vendors can provide and what may be the facilitator/inhibitors of such activities.Design/methodology/approach – A case describing the setting up of an offshore analytics operation is presented, which gives a backdrop to the challenges faced in relatively high end value creation processes in a remote outsourced (offshore) environment. This provides some empirical support to a proposed model for facilitating the outsourcing of value‐added services.Findings – A model is proposed for determining the degree to ...

A brand share prediction model based on several disparate sources of data: an empirical model of detergent choice in Mumbai, India

We describe the application of a nested logit function for modelling consumer brand choice using household transaction data from the Indian market. This is unique since it is one of the first attempts to integrate disparate consumer information sources available at various levels of aggregation towards developing a prediction model for brand market share in India. We test the usefulness of the model for forecasting brand market share in the premium detergents market in Mumbai, India. The results of the model building exercise reveal the importance of advertising, specifically the role of ad message in influencing brand choice. It is concluded that such modelling initiatives show significant returns for market planning exercises in developing markets. However, the need for streamlining the collection of market data and its subsequent organization in a form that can help develop more portent prediction models is apparent.

A choice modelling approach to evaluate effectiveness of brand development initiatives

We describe the application of a nested logit function for modelling brand choice using data from disparate data sources in a large emerging market (India). This is unique since it is one of the first attempts to integrate disparate consumer information sources available at various levels of aggregation towards developing a prediction model for brand market share. We further develop a methodology for brand market share decomposition into components that can be attributable to various explanatory variables. The implications are significant since this methodology helps in using behavioural tracking data towards developing a decision tool to evaluate marketing programmes.

Shiv Industries Facing the Challenge of Global Competition

This is an integrative case that brings out the intricacies of globalization and provides scope for discussion on the alternate strategies Shiv Industries can take to successfully operate in a new environment. The case highlights the problems of the organized sector in the changing market context where these companies face challenges from both ends—at the lower end competition comes from the unorganized sector whereas at the higher end of technology competition comes from more evolved industries in the current Indian scenario.

Determinants of Analytics Process Adoption in Emerging Economies: Perspectives from the Marketing Domain in India:

Decision-making based on market information has been the hallmark of many successful businesses over decades. The need for precision and specificity in decision rules has been the motivator for policy-makers in organizations to adopt analytical processes to extract insights about markets. Traditionally, the marketing research function in organizations spearheaded the collection and analysis of sampled market data and feeding inferences to decision-makers. Over time, market measurement infrastructure has improved significantly (and is automated) for organizations to start collecting market-level data continuously, comprehensively, and on a large scale (Nielsen, 2015). In many instances today, the data gets collected not by choice but as a result of process output.