Department of Marketing & Entrepreneurship

    One of the major trends in the software industry over the last few years has been the migration of software firms from the perpetual licensing business model to the ''Software as a Service'' (SaaS) model. In the SaaS model, the software is sold as a service, i.e., consumers can subscribe to the software based on monthly or annual contracts. Global revenues for the SaaS industry now exceed 200 billion USD (Gartner, 2019). This shift in the business model has fundamentally changed the marketing and promotional activities of software firms. In particular it has allowed firms to leverage a new type of customer acquisition strategy: free trial promotions, where new users are given a limited time to try the software for free.
    Free trials are now almost universal in the SaaS industry because softwares are inherently experience goods, and free trials allow consumers to try the software product without risk. However, we do not have a good understanding of how long these trials should be and/or the exact mechanism through they work. In the industry, we observe trial lengths ranging anywhere from one week to three months e.g., Microsoft365 offers a 30 days free trial whereas Google's G Suite offers a 14 days free trial. There are pros and cons associated with both long and short trials. A short trial period is less likely to lead to free riding or demand cannibalization, and is associated with lower acquisition costs. On the other hand, a long trial period can enhance consumer learning by giving them more time to learn about product features and functionalities. Longer trials can also create stickiness/engagement and increase switching-back costs. That said, if users do not use the product more with a long trial, they may simply conclude that the product is not useful and/or forget about it. Thus, longer trials lack the deadline or urgency effect (Zhu et al., 2018).
    While the above arguments make a global case for shorter/longer trials, the exact mechanism at work and the magnitude of its effect can be heterogeneous across consumers. In principle, if there is significant heterogeneity in consumers' response to the length of free trials, then SaaS firms may benefit from assigning each consumer a different trial length depending on her/his demographics and skills. The idea of personalizing the length of free trial promotions based on consumer demographics is akin to implementing a third-degree price discrimination scheme because we are effectively offering different prices to different consumers over a fixed period of time. Indeed, SaaS free trials are particularly well-suited to personalization because of a few reasons. First, software services have zero marginal costs and there are no direct cost implications of offering different trial lengths to different consumers. Second, it is easy to implement a personalized free trial policy at scale for digital services, unlike physical products. Finally, consumers are less likely to react adversely to receiving different free trial lengths (unlike other marketing mix variables, e.g., prices). However, it is not clear whether personalizing the length of free trials improves customer acquisition and firm revenues, and if yes, what is the best approach to design and evaluate personalized free trials.

Firms always want to know whether a higher pay can incentivize workers to improve their performance. This question is hard to answer since worker’s pay is usually endogenously determined. In this paper, we answer this question making use of a field experiment run by a large online education company which randomizes the hourly pay of a group of workers. The company recruits native English speakers and provides an online platform for them to teach Chinese kids spoken English. We find that workers who get a higher hourly pay not only open more classes, but their classes are also more likely to be booked by students, and these gaps are increasing over time. Based on detailed data from video and audio analytics and limited data on class rating, we identify the key factors influencing worker’s performance in a class, and we construct a score that integrates these factors and measures worker’s performance in each class. We find that workers who get a higher hourly pay do not have a higher performance score initially, but their performance score increases faster than those who get a lower hourly pay, indicating that workers with a higher hourly pay are incentivized to learn the key influencing factors and improve their performance in a faster way than those with a lower hourly pay.

Inclusive design considers the needs and capabilities of the whole population to decrease the actual or perceived mismatch between the user and the design object. We review the inclusive design literature across multiple disciplines to conceptualize inclusive design, identify who should be included in the inclusive design process, present an overview of the evolution of design approaches, and summarize best practices on how organizations can facilitate inclusive design. We posit three levels of inclusive design based on the diminishing degree of mismatch between the user and the design object: providing accessibility (Level 1), engaging participation by creating equitable experiences (Level 2), and facilitating empowered success via flow experiences (Level 3). We introduce our Design, Appraisal, Response, Experience (DARE)framework to explain the complex cognitive appraisals and emotional responses that each of these three levels of inclusive design elicits and underscores the notion that inclusive design works best when it's not intended for a specific need, but rather benefits anyone who uses it. We conclude with a call for future research in this rich and important domain of investigation that seeks both to understand consumer response to inclusive design and to incorporate inclusive design into brand strategy, practice, and policy.

Many online marketplaces offer sponsored product listings that are resemblant to and located within organic product listings, as a type of native advertising for the third-party sellers. The potential benefits of sponsored listings to the sellers are non-trivial and position-specific, as online marketplaces display organic listings in order of relevance rankings and consumers may avoid to click an advertised product. This paper investigates a field-experimental dataset from a large online marketplace, where both exposure and position of sponsored listings were randomized. It finds that the products in upper positions are less likely to be clicked with the signal of ads, whereas the products in lower positions are more likely to be clicked with the signal of ads. The results suggest that the sellers' incentives depend on the organic positions, as consumers' coping strategy with sales advertising may vary by their position-specific expectations of inherent relevance.

Owing to the rapid and sustained rise of digital media channels, media planning is getting more complex by the day. However, advertising budgets have not kept pace with the growth in media channels and this forces marketing managers to weigh up the costs and benefits of the proliferating number of media outlets. Historically, advertising allocation decisions operated mostly at a macro level, comprising determination of the total budget followed by apportionment among media. Today, an additional decision operates at a micro level, namely, which specific customers to target with advertising. In the macro case, optimal control theory provides a powerful framework for firm profit maximization allowing for ad response, cost per medium, and discount rate, all in the presence of multiple competing brands. However, optimal control theory has never been applied to the situation of micro targeting individual customers. Consequently, in this study we show how optimal control theory can be adapted for application to individual customers by using a multinomial logit model with individual-specific advertising response parameters. In turn, these parameters are used to determine the optimal number of exposures each customer should receive for each advertising medium. Using simulations, we show that micro targeting becomes more profitable as customer advertising response heterogeneity increases. We then demonstrate with an empirical example that using our optimal micro targeting method improves profits over existing ad scheduling methods by between 116% and 146%.

This conversation will center on the nature of health care, healthcare research and how it creates but also constrains opportunities for doing research that will be found valuable in the scholarly marketing community. As a substantive area, there are plenty of behavioral questions that center both the patient and the firm, and there is considerable amount of secondary data, both of which represents an upside. However, there are significant structural challenges that need to be transformed into virtues for them to gain traction within the mainstream marketing journals. I will describe my experiences with a few projects, share the backstory as to why I got involved, and engage with you as to how they can be framed/constructed into something more than research that fits a health care special issue.