Information design—or the practice of effectively communicating information to its audience—is a delicate dance that requires accuracy, clarity, comprehensiveness, and engagement. Yet, even when all these boxes are checked off, it is no secret that people process information differently. The different ways people interpret and assimilate information, in turn, lead to a wide, non-uniform range of choices and decisions. Such is also the case in firm operations. Many firms serve a broad spectrum of customers with different needs, preferences, and behaviors. As a result, although it is crucial for firms to examine how different types of information design affect consumer perceptions and behavior, customer heterogeneity often hinders firms from measuring the impact of information design at a holistic, systemic, and operational level. This dissertation seeks to tackle such research question; specifically, the dissertation examines how different information design choices affect consumer perceptions, consumer engagement, and firm operations. The dissertation leverages methods and techniques in field experiments, causal inference, machine learning, and lab experiments. The dissertation consists of three distinct essays. The first chapter explores how providing transparency into tradeoffs affects customer acquisition, engagement, and retention, through running a field experiment with 393,036 customers considering applying for bank-issued credit cards. The results reveal that—although providing transparency into tradeoffs did not have a significant effect on customer acquisition rates—customers who were exposed to the transparency exhibited increased product usage, higher retention, and lower likelihood of making late payments. In the presence of a promotional campaign that provides financial incentives, however, the positive effects of tradeoff transparency were attenuated. Moreover, the findings suggest that the effect of tradeoff transparency on consumer spending and retention is more pronounced among the customers with more familiarity and experience with credit card products. Overall, the results show that disclosing tradeoffs can be an effective strategy for firms to keep the customers better informed and improve customer engagement. The second chapter examines how different facets of eCommerce delivery data can be used to develop and improve delay prediction models. We apply a mix of causal inference and machine learning (e.g., random forest) models to a comprehensive, large-scale dataset spanning user, delivery, and order information from JD.com, one of the largest eCommerce companies in China. In doing so, we first analyze how duration of each leg of the delivery, time allotted for each leg, and probability of delay relate to each other. Then, we fit random forest models to predict delays and identify primary predictors for such delays. Testing random forest models with different feature sets shows that including information about the earlier leg or warehouse package load can significantly improve the accuracy of the prediction model. Our prediction models suggest that managers can leverage various operational data to identify delays early on to prevent the orders from being delayed. Finally, the third chapter seeks to tackle the perennial issue in consumer contracts: the ‘no-reading problem.’ Past studies have shown that somewhere between 74% and 99.8% of readers skip reading consumer contracts (or “fine print” or “terms and conditions”). In this chapter, we propose—and evaluate the effectiveness of—showing simpler translations of long, convoluted consumer contracts alongside the original version. Results of four experimental studies suggest that highlighting the key points of each clause of the contract significantly improves consumers’ perceptions of the company, trust, and willingness to sign contracts. However, these effects were attenuated in the presence of risk associated with the contract and its associated service or product. We conclude the chapter with a general discussion on how managers and firms can best put our research findings into practice.
