Business Analytics

Customer Relationship Management (CRM) campaigns have traditionally focused on maximizing the profitability of the targeted customers. In this paper we investigate the social effects of CRM campaigns. We demonstrate that, in business settings that are characterized by network externalities, a CRM campaign that is aimed at changing the behavior of specific customers propagates through the social network, thereby also affecting the behavior of non-targeted customers.

We model the multifaceted impact of pricing decisions in B2B contexts and show how a seller can develop optimal inter-temporal targeted pricing strategies to maximize long-term customer value. We empirically model the B2B customer's purchase decisions in an integrated fashion. In order to facilitate targeting and to capture the short and long-term dynamics of B2B customer purchasing, our modeling framework weaves together in a hierarchical Bayesian manner, multivariate copulas, a non-homogeneous hidden Markov model, and control functions for price endogeneity.

Web 2.0 provides gathering places for internet users in blogs, forums, and chat rooms. These gathering places leave footprints in the form of colossal amounts of data regarding consumers' thoughts, beliefs, experiences, and even interactions. In this paper, we propose an approach for firms to explore online user-generated content and "listen" to what customers write about their and the competitors' products. Our objective is to convert the user-generated content to market structures and competitive landscape insights.

In this research we propose a web-based adaptive self-explicated approach for multi-attribute preference measurement (conjoint analysis) with a large number (ten or more) of attributes. Our approach overcomes some of the limitations of previous self-explicated approaches. We developed a computer-based self-explicated approach that breaks down the attribute importance question into a sequence of constant-sum paired comparison questions.

The U.S. pharmaceutical industry spent upwards of $18 billion on marketing drugs in 2007. Detailing and drug sampling activities account for the bulk of this spending. To stay competitive, pharmaceutical managers need to maximize the return on these marketing investments by determining which physicians to target, when, and how to target them. In this paper, we present a two-stage approach for dynamically allocating detailing and sampling activities across physicians to maximize long-run profitability.

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.

An idea is a collection of existing concepts or words. What makes an idea original or appealing is how these concepts or words are combined in the context in which they appear. Similarly, a food recipe is a combination of ingredients, and it is often evaluated based on how these ingredients fit together to form the whole. In this research, we leverage representation learning methods, specifically word embeddings, to measure the fit among ingredients in the recipe and capture the possibly complex interactions between these ingredients.

Generative AI’s recent advancements in creating content have offered vast potential to transform the advertising industry. This research investigates the impact of generative AI-enabled visual ad creation on real-world advertising effectiveness. For this purpose, we collaborate with a display ad platform and leverage a quasi-experimental setting that includes over two million ad-day observations by over seven thousand advertisers across nearly 50 product categories, encompassing more than 16 billion ad impressions and 116 million clicks.