Modeling Multimodal Continuous Heterogeneity in Conjoint Analysis — A Sparse Learning Approach

Consumers\u27 preferences can often be represented using a multimodal continuous heterogeneity distribution. One explanation for such a preference distribution is that consumers belong to a few distinct segments, with preferences of consumers in each segment being heterogeneous and unimodal. We propose an innovative approach for modeling such multimodal distributions that builds on recent advances in sparse learning and optimization. We apply the model to conjoint analysis where consumer heterogeneity plays a critical role in determining optimal marketing decisions. Our approach uses a two-stage divide-and-conquer framework, where we first divide the consumer population into segments by recovering a set of candidate segmentations using sparsity modeling, and then use each candidate segmentation to develop a set of individual-level heterogeneity representations. We select the optimal individual-level heterogeneity representation using cross-validation. Using extensive simulation experiments and three field data sets, we show the superior performance of our sparse learning model compared to benchmark models including the finite mixture model and the Bayesian normal component mixture model

Consumer Dynamic Usage Allocation and Learning under Multi-Part Tariffs

Multipart tariffs are widely favored within service industries as an efficient means of mapping prices to differential levels of consumer demand. Whether they benefit consumers, however, is far less clear as they pose individuals with a potentially difficult task of dynamically allocating usage over the course of each billing cycle. In this paper we explore this welfare issue by examining the ability of individuals to optimally allocate consumption over time in a stylized cellular-phone usage task for which there exists a known optimal dynamic utilization policy. Actual call behavior over time is modeled using a dynamic choice model that allows decision makers to both discount the future (be myopic) and be subject to random errors when making call decisions. Our analysis provides a “half empty, half full” view of intuitive optimality. Participants rapidly learn to exhibit farsightedness, yet learning is incomplete with some level of allocation errors persisting even after repeated experience. We also find evidence for an asymmetric effect in which participants who are exogenously switched from a low (high) to high (low) allowance plan make more (fewer) errors in the new plan. The effect persists even when participants make their own plan choices. Finally, interventions that provide usage information to help participants eradicate errors have limited effectiveness

NETWORK STABILITY AND SOCIAL CONTAGION ON THE MOBILE INTERNET

We study the dual roles of the stability of an individual’s social network and social contagion on individual behavior in the mobile Internet setting. We use a panel dataset containing all mobile records for a sample of 3G mobile subscribers. Our data includes information about their frequency of mobile Internet usage, and their communication patterns across voice calls and messages, which allow us to map any dynamics in their social network. We find three main results. First, users with high network stability have a low intrinsic tendency to engage in content usage and generation on the mobile Internet. Second, the extent of positive social contagion effect is mitigated for users with stable networks. Third, we find that network stability is a significant predictor for individual behavior even after controlling for network closure. We discuss the implications of these findings for social network theory, social contagion and managerial practice

Toward Effective Social Contagion: A Micro Level Analysis of the Impact of Dyadic Network Relationship

Social advertising holds the potential to reshape the traditional advertising industry. Understanding what leads to effective social contagion at the dyadic level lies at the core of cost-effective social advertising strategies. This paper is the first attempt to comprehensively study the effect of dyadic network relationship on social contagion in directed networks. This paper reveals several intriguing findings of great importance to social media marketers: (1) reciprocal followers of adopters are less likely to be influenced than non-reciprocal followers, as moderated by the popularity and novelty of information; (2) social media users pay attention to their followers’ tastes while making the adoption decision; (3) the number of common mutual followers has opposite effects on the dyadic influence between reciprocal (positive) and non-reciprocal (negative) ties. In addition, this paper provides a novel model to identify social influence when adoption events are caused by multiple sources

Choice Models and Customer Relationship Management

Customer relationship management (CRM) typically involves tracking individual customer behavior over time, and using this knowledge to configure solutions precisely tailored to the customers' and vendors' needs. In the context of choice, this implies designing longitudinal models of choice over the breadth of the firm's products and using them prescriptively to increase the revenues from customers over their lifecycle. Several factors have recently contributed to the rise in the use of CRM in the marketplacePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47023/1/11002_2005_Article_5892.pd