AI@CBS

In 2009, Samsung invited Professor Bernd Schmitt to advise the company about how it could transform its brand and appeal to consumers on an emotional level. Although Samsung was generally well-regarded for its technological skill and steady execution, it had a reputation for lacking innovation and creativity. In this case, students accompany Schmitt on his journey from Seoul to Silicon Valley to Las Vegas as he discovers how a revolutionary new camera might change consumers' perception of the brand.

Lawrence Glaubinger, a Columbia Business School alumnus, became increasingly involved in running his 100-acre Christmas tree farm after he retired in 1992. Between 1993 and 2008, Glaubinger sold Ponderosa pines through an arrangement with Green Acres, a large retailer that bought Glaubinger's trees to sell at its nurseries. While Glaubinger Tree Farm sold the trees to Green Acre at a wholesale price based on its marginal cost, the retailer independently set retail pricing. Glaubinger suspected his tree farm could have shown higher profits if it had sold directly to consumers.

Anchoring refers to a biased judgment on a stimulus based on the initial assessment of another stimulus and the insufficient adjustment away from that initial assessment. Previous research indicates that anchoring seems to be a general phenomenon, underlying a wide variety of processing strategies (Epley and Gilovich 2001; Johnson and Puto 1987; Tversky and Kahnemann 1974). Every time when individuals form an impression or an image about a stimulus while another stimulus is present, these impressions may be subject to anchoring effects.

This research models the dynamics of customer relationships using typical transaction data. Our proposed model permits not only capturing the dynamics of customer relationships but also incorporating the effect of the sequence of customer-firm encounters on the dynamics of customer relationships and the subsequent buying behavior. Our approach to modeling relationship dynamics is structurally different from existing approaches.

We propose a framework for designing adaptive choice-based conjoint questionnaires that are robust to response error. It is developed based on a combination of experimental design and statistical learning theory principles. We implement and test a specific case of this framework using Regularization Networks. We also formalize within this framework the polyhedral methods recently proposed in marketing.

We identify gaps and propose several directions for future research in preference measurement. We structure our argument around a framework that views preference measurement as comprising three interrelated components: (1) the problem that the study is ultimately intended to address; (2) the design of the preference measurement task and the data collection approach; (3) the specification and estimation of a preference model, and the conversion into action. Conjoint analysis is only one special case within this framework.

Companies need tools and models that accurately describe and forecast the diffusion process of new products. Most tools for predicting the diffusion of an innovation, given early data, use aggregate models (such as the Bass model) that capture the impact of advertising and word-of-mouth on adoption. Because of their aggregate nature, however, (i.e., these models treat each month or each year as one observation), they are often unable to produce reliable predictions early in the diffusion process, when such predictions would be most helpful (e.g., in order to adjust the launch campaign).

Following a successful idea generation exercise, a company might easily be left with hundreds of ideas, generated by experts, employees, or consumers. The next step is to screen these ideas, and identify those with the highest potential. In this paper we propose a practical approach to involving consumers in idea screening. Although the number of ideas may potentially be very large, it would be unreasonable to ask each consumer to evaluate more than a few ideas. This raises the challenge of efficiently selecting the ideas to be evaluated by each consumer.