Modern marketplaces, enabled by recent advances in information technology, are becoming ever more complex, dynamic and interconnected. Customers are increasingly more informed about the choices available to them and are thus able to make better decisions. For firms, this presents both opportunities and challenges. On the positive side, the firm has a bigger than ever array of tools at its disposal that enable it to better sell to its customers. Such tools are both computational (pricing algorithms, customer targeting data, social networking information) and economical (dynamic mechanisms) in nature. The challenging aspect for the firm is that modern technology also allows consumers to easily learn the opinions of fellow customers and to be strategic in their decision-making. Having such strategic and networked customers is not necessarily a net negative for the firm, but it certainly makes the firm's problem of how to sell its products a more complex one.
This project aims to develop a framework for understanding how to sell goods in markets that are fundamentally dynamic and interconnected. The project will research what mechanisms are revenue-optimal (or near-optimal, while being simple and computationally tractable) in dynamic settings where customers can learn from each other. The PIs will study the effect of network learning on the firm's behavior and whether it forces the firm to share some of the costs associated with consumer learning. The project will also focus on how the social networkstructure affects the optimal mechanism and will try to understand whether such network learning effects lead to lower (or higher) revenue for the firm and aggregate consumer welfare.
Fundamental research in the dynamics of networked markets provides deeper knowledge to a broad audience that includes firms, consumer groups and regulators. Curriculum development at the interface of operations research, information systems and computer science will benefit from this research experience.
Word-of-mouth plays a central role in the propagation of brand or new product information and as such it is a first order consideration in the design and implementation of a firm’s pricing and marketing strategy. Information about a brand or a new product evolves dynamically over time as agents experiment with it. The main goal of this project is to develop an understanding on the optimal way a monopolist can assist her customer base experiment with a new product and gradually learn its quality through her pricing and marketing strategies. A crucial feature in our model is that customers may be organized in a social network, which further implies that they can only exchange information with their local neighbors. A natural question that arises is how to use limited resources, e.g., marketing budget, to optimally spread information about the new product via the word-of-mouth process. Our results along this direction identify a notion of network centrality that is crucial in obtaining a characterization of optimal strategies: firms should target most of their budgets to central agents, who in turn amplify the effects of the firm’s marketing efforts by spreading information to their peers. Knowing the underlying network structure and being able to target at an individual level is more critical and valuable for a firm for networks that feature high levels of heterogeneity, i.e., they have information mavens and connectors that are able to assimilate and distribute a large amount of information. So far the focus was on devising optimal ways for a firm to "seed" a network, i.e., provide agents with information about the new product and then let them spread this information to their peers. Another related direction concerns the incentives for consumers to generate information via their own experimentation. A major hurdle to experimentation is free-riding: if one knows that her peers are experimenting with a new product, then she may forgo her own experimentation thus leading to very low levels of aggregate experimentation. Typically, consumers have access to review websites that act as information aggregators. We show that the presence of such aggregators may in fact adversely affect the consumers’ incentives for experimentation: the more readily available information is, the stronger the incentives to free-ride on others are. Thus, that necessitates looking carefully at how to disclose information about experimentation outcomes. Our results indicate that slowing down the rate at which information is revealed to the public eventually increases overall welfare as consumers tend to rely more on their own efforts. Although we framed the project in the context of a monopolist selling to a networked market, our results have implications for settings where the objective is not increasing awareness about a new product but rather promoting a welfare-enhancing policy or behavior. For example, our findings can be useful when designing a campaign to promote healthy food habits. Our goal is to continue looking into these issues with the ultimate goal of completing our understanding on how a central entity (planner, monopolist) can incentivize a social network of agents to experiment, generate and spread information about a new product (policy). We already made some progress towards this direction but there are many more unexplored questions.
