EXPRESS: The Economic and Social Impacts of Migration on Brand Expenditure: Evidence from Rural India

Households sending members to work away from home often receive information about lifestyles and consumption behaviors in those migration destinations (i.e., social remittances) along with economic remittances. We investigate the effect of having a migrant household member on household brand expenditures in rural India—a market characterized by substantial consumption of unbranded products. We collect and analyze household-level survey data from 434 households across 30 villages using an instrumental variable strategy. Economic remittances result in greater brand expenditure and this level is higher for poorer households. After controlling for economic remittances, the effect of migration on brand expenditures is more positive for households residing in more populous villages, with greater access to mobile phones, lower viewership of television media, and with less recently departed migrants. We demonstrate how marketing resource allocation across villages can be improved by incorporating migration data and provide insights for household targeting in the context of door-to-door selling in villages. Our results are robust to alternate, public policy-based instruments, and can be generalized to expenditure on private schools. Using additional survey data from 300 households in 62 new villages, we replicate our results by comparing within-households brand expenditures before and after the migration event.

Comparing alternative approaches to estimate customer equity

Purpose The purpose of this paper is to explore the differences and similarities between two methods/models for estimating customer equity (CE): one using behavior-based data and one using market-based data. Design/methodology/approach Two separate analyses of the same market scenario (telecom industry) were conducted, by applying the CE estimation method from Rust et al. (2004) and the CE model from Gupta et al. (2004). Findings Different methods/models can produce similar estimates, which corroborates the defense of an integrated multi-method approach to evaluating CE. In addition, they each provide different benefits. The behavioral data model provides identification of CE drivers and assists in the task of marketing resource allocation, the market-based data model is simple and easy to implement and is recommended in cases when CE is used as a financial indicator. Originality/value This paper contributes to the CE literature in the following ways. First, it demonstrates the possibility of obtaining similar estimates of CE using distinct types of data and data collection procedures, and with two different estimation methods/models. Second, it confirms that either model allows firms to compute the expected market capitalization at any given time using customer and financial information. Third, it demonstrates the convergent validity of these two methods/models for estimating CE for either public or private companies, thus legitimizing the comparison of their respective CE values, regardless of the type of source data or estimation formula used.

A hybrid model for customer portfolio analysis in retailing

Purpose Marketing/finance interface and application of its new insights in marketing decisions have recently found great interest among marketing researchers and practitioners. There is a relatively large body of marketing literature about incorporating modern portfolio theory (MPT) into customer portfolio context and taking advantage of it in marketing resource allocation decisions. Previous studies have modelled customer portfolio risk in the form of historical return/profitability volatility of customer base. However, the risk is a future-oriented measure, and deals with future volatility associated with return stream. This study aims to address this research problem. Design/methodology/approach The well-known Pareto/non-binomial distribution (NBD) approach is used to model customer purchases in a non-contractual setting of research practice. Then, the results were used to simulate the customers’ future buying behaviour and associated returns via the Monte Carlo simulation approach. Subsequently, the mean-variance portfolio optimization model was applied to find the optimal customer portfolio mix. Findings The results illustrated the better performance of the proposed efficient portfolio versus the current customer portfolio. These results are applicable in analyzing customer portfolio composition, and can be used as a guidance to make decisions about marketing resource allocation in different segments. Originality/value This study proposes a new approach to analyze customer portfolio by using the customers’ future buying behaviour. Taking advantage of rich marketing literature about statistical assumptions describing the customers’ buying behaviour, this study tries to take some steps forward in the application of the MPT theory in customer portfolio management context.

Sales Forecast for Garment Companies with Cluster Analysis and Modified Neural Networks

For the companies of the garment industry, managers often dedicate their efforts to forecast the sales accurately while making decisions for marketing resource allocation and scheduling. Based on the historical database, this paper constructs a method to investigate the relationship of the relating factors and sales values. The proposed method combines the cluster analysis and modified neural networks to fulfill the sales forecast task. Firstly, the average linkage cluster algorithm is applied to cluster similar sales values. Secondly, a modified neural network is used to investigate the mapping relationship between those influencing factors and the sales clusters. The method employs a self-adjust mechanism to determine the structure of the neural network. The effectiveness of the proposed method is illustrated with a case study of a garment company in Shanghai.