Marketing and Data Science: Together the Future is Ours

The synergistic use of computer science and marketing science techniques offers the best avenue for knowledge development and improved applications. A broad area of complementarity between the typical focus in statistics and computer science and that in marketing offers great potential. The former fields tend to focus on pattern recognition, control and prediction. Many marketing analyses embrace these directions, but also contribute by modeling structure and exploring causal relationships. Marketing has successfully combined foci from management science with foci from psychology and economics. These fields complement each other because they enable a broad spectrum of scientific approaches. Combined, they provide both understanding and practical solutions to important and relevant managerial marketing problems, and marketing science is already very successful at obtaining unique insights from big data

Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions

An online recommendation system (RS) involves using information technology and customer information to tailor electronic commerce interactions between a business and individual customers. Extant information systems (IS) studies on RS have approached the phenomenon from many different perspectives, and our understanding of the nature and impacts of RS is fragmented. The current study reviews and synthesizes extant empirical IS studies to provide a coherent view of research on RS and identify gaps and future directions. Specifically, we review 40 empirical studies of RS published in 31 IS journals and five IS conference proceedings between 1990 and 2013. Using a recommendation process theoretical framework, we categorize these studies in three major areas addressed by RS research: understanding consumers, delivering recommendations, and the impacts of RS. We review and synthesize the extant literature in each area and across areas. Based on the review and synthesis, we surface research gaps and provide suggestions and potential directions for future research on recommendation systems

Dynamic preference elicitation of customer behaviours in e-commerce from online reviews based on expectation confirmation theory

Preference change, also known as preference drift, is one of the factors that online retailers need to consider to accurately collect consumer preferences and make personalised recommendations. Online reviews have been widely used to analyse the preference drift of consumers. However, previous studies on online reviews ignored the psychological perceptions of consumers in terms of satisfaction. This paper aims to develop a method for dynamic preference elicitation from online reviews based on exploring the theory of consumer satisfaction formation. Based on the framework of expectation confirmation theory, we develop formulas for expressing the relations among expectation, perceived performance, confirmation, and satisfaction. We then use the proposed dynamic preference elicitation model to predict the change of consumer overall preference after each review and rank products for consumers’ next purchase. We test the proposed approach with a case study based on a data set from Amazon.com. It is founded that the satisfaction changes in each purchase, and this change will affect the prediction of the next product ranking. The case study is based on one product group, and further research is needed to see if the operation of the proposed method can be extended to other kinds of product

A systematic review of artificial intelligence and robots in value co-creation: Current status and future research avenues

As artificial intelligence (AI) and robots are increasingly taking place in practical service solutions, it is necessary to understand technology in value co-creation. We conducted a systematic literature review on the topic to advance theoretical analysis of AI and robots in value co-creation. By systematically reviewing 61 AI and robotics articles, which have been published in top marketing and service research journals, we identified four themes in literature, namely generic field advancement, supporting service providers, enabling resource integration between service providers and beneficiaries, and supporting beneficiaries’ well-being. With the identification of the first set of literature on AI and robots in value co-creation, we push forward an important sub-field of value co-creation literature. In addition, to advance the field, we suggest building on Actor-Network Theory (ANT) and Science and Technology Studies (STS) to understand the agency of technology in value co-creation. Considering that technology has agency opens new interesting research avenues around shopping bots and human-to-non-human frontline interaction that are likely to influence resource integration, customer engagement, and value co-creation in the future. We also encourage our colleagues to conduct postphenomenological research to be better geared for analyzing how technology (incl. AI and robots) mediates the individual experience of value.</p

Consideration behavior and design decision making

Over the past decade, design engineering has developed a systematic framework to coordinate with consumer behavior models. Traditional consumer models applied in the past has mainly focused on the preference of compensatory trade-offs in the choice decisions. Recent marketing research has become interested in developing consumer models that are representative in that they reflect realistic human decision processes. One important example is consideration : the process of quickly screening out many available alternatives using non-compensatory rules before trading off the value of different feature combinations. Is capturing consideration important for design? This research investigates the impact of modeling consideration behavior to design engineering, aiming at constructing consideration models that can inform strategic decisions. The study includes several features absent in existing research: quantifying the mis-specifications of the underlying choice process, tailoring survey instruments for particular models, and exploring the models\u27 strategic value on product profitability and design feature differences. First, numerical methods are explored to address the discontinuity in the profit-oriented optimization problem introduced by the consideration models. Methods based on complementarity constraints, smoothing functions and genetic algorithms are implemented and evaluated with a vehicle design case study. Second, a simulation experiment based on synthetic market data compares consideration models and a variety of conventional choice models in the process of model estimation and design optimization. The simulation finds that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to sub-optimal design decisions when the population uses consideration behavior; convergence of compensatory models to non-compensatory behavior is likely to require unrealistic amounts of data; modeling heterogeneity in non-compensatory screening is more valuable than heterogeneity in compensatory trade-offs. The synthetic experiment framework then further extends the comparison to include the survey design process guided by the different assumptions behind considerations and traditional models. A product line design case study reveals that even though both compensatory models and consideration models show robustness in profitability, using consideration models leads to optimal portfolios with higher feature diversity while reducing the risk of overestimating profits. Finally, the research explores how to use consideration models to analyze the market penetration of newly designed product in a case study of a consideration maximization problem. It is the hope that this research will arouse the attention of designers to the informative power of consideration models, expand the understanding of consumer behavior modeling from the predictive power in the marketing field to the strategic impacts to design decisions, and provide technical support to the future application of consideration models in design engineering

Three essays on modeling consumer demand

Scope, Method of Study, and Findings: Although stated preference data obtained through real and hypothetical surveys and experiments are increasingly being used in economic research, there remain doubts about the validity of preference elicitation methods and econometric models used to estimate consumer preferences. This study explores such doubts and provides richer understanding of consumer demand.The first essay compared the ability of three preference elicitation methods (hypothetical choices, non-hypothetical choices, and non-hypothetical rankings) and three discrete-choice econometric models (the multinomial logit [MNL], the independent availability logit [IAL], and the random parameter logit [RPL]) to predict actual retail shopping behavior in three different product categories (ground beef, wheat flour, and dishwashing liquid). Overall, this study found a high level of external validity. Specific results suggested that the non-hypothetical elicitation approaches, especially the non-hypothetical ranking method, outperformed the hypothetical choice experiment in predicting retail sales. This study also found that the RPL can have superior predictive performance, but that the MNL predict equally well in some circumstances.Although experimental studies have reported a wide array of other-regarding behavior, the pervasiveness of such behavior in the field is an open question. Using a stated preference experiment, the second essay first estimated people's preferences, when purchasing food products, for the distribution of benefits accruing to participants in the food supply chain. Although none of the existing fairness models exhibit much predictive power, this study found that people are in-fact concerned about the distribution of benefits resulting from food purchases, and that modifications to the models to fit the food context significantly improves explanatory power. Finally, this study found that the measured preferences, along with elicited beliefs are significant predictors of people's willingness-to-pay a premium for organic food.RPL or mixed logit models are increasingly being reported in the literature, but uncertainties about the reliability of the model remain. Using Monte Carlo simulations, the third paper found that mixed logit estimates exhibit bias in small samples (N=200); an effect that dissipates as sample size increases. Despite the fact that the conventional multinomial logit is a restricted form of the mixed logit, simulation results indicate that if there is no preference heterogeneity, the conventional multinomial logit provides much more efficient estimates than the mixed logit. Moreover, in large samples there is a high likelihood of observing type I errors - finding statistically significant heterogeneity when none exists. Finally, simulation results indicate differences in performance across commercial software package providing mixed logit estimation routines