Brand positioning and consumer taste information

Firms commonly run field experiments to improve their freemium pricing schemes. However, they often lack a framework for analysis that goes beyond directly measurable outcomes and focuses on longer term profit. We aim to fill this gap by structuring existing knowledge on freemium pricing into a stylized framework. We apply the proposed framework in the analysis of a field experiment that contrasts three variations of a freemium pricing scheme and comprises about 300,000 users of a software application. Our findings indicate that a reduction of free product features increases conversion as well as viral activity, but reduces usage – which is in line with the framework’s predictions. Additional back-of-the-envelope profit estimations suggest that managers were overly optimistic about positive externalities from usage and viral activity in their choice of pricing scheme, leading them to give too much of their product away for free. Our framework and its exemplary application can be a remedy.

Consumers often do not have complete information about the choices they face and therefore have to spend time and effort in acquiring information. Since information acquisition is costly, consumers have to trade-off the value of better information against its cost, and make their final choices based on imperfect information. We model this decision using the rational inattention approach and describe the rationally inattentive consumer’s choice behavior when she faces options with different information costs. To this end, we introduce an information cost function that distinguishes between direct and inferential information. We then analytically characterize the optimal behavior and derive the choice probabilities in closed-form. We find that non-uniform information costs can have a strong impact on product choice, which gets particularly conspicuous when the product alternatives are otherwise very similar. It can also lead to situations where it is disadvantageous for the seller to provide easier access to information for a particular product. Furthermore, it provides a new explanation for strong failure of regularity of consumer behaviour, which occurs if the addition of an inferior – never chosen – product to the choice set increases the market share of another existing product.

Many industries, including consumer electronics and telecommunications equipment, are characterized with short product life-cycles, constant technological innovations, rapid product introductions, and fast obsoles- cence. Firms in such industries need to make frequent design changes to incorporate innovations, and the effort to keep up with the rate of technological change often leaves little room for the consideration of product reuse. In this paper, we study the design for reusability and product reuse decisions in the presence of both a known rate of incremental innovations and a stochastic rate of radical innovations over time. We formulate this problem as a Markov Decision Process. Our steady-state results conrm the conventional wisdom that a higher probability of radical innovations would lead to reductions in the rm's investments in reusability as well as the amount of reuse the rm ends up doing. Interestingly, the design for reusability decreases much more slowly than the actual reuse. We identify some specic scenarios, however, where there is no tradeoff between the possibility of radical innovations and the rms reusability and reuse decisions. Based on over 425,000 problem instances generated over the entire range of model parameters, we also provide insights into the negative impact of radical innovations on rm prots, but show that the environmental impact of increased radical innovation is not necessarily negative. Our results also have several implications for policy makers seeking to encourage reuse.

We study the optimal pricing problem of a firm facing customers with limited attention and capability to process information about the value (quality) of the offered products. We model customer choice based on the theory of rational inattention in the economics literature, which enables us to capture not only the impact of true qualities and prices, but also the intricate effects of customer’s prior beliefs and cost of information acquisition and processing. We formulate the firm’s price optimization problem and characterize the pricing and revenue implications of customer’s limited attention. We test the robustness of our results under various modelling generalizations such as prices signalling quality and customer heterogeneity, and study extensions such as multiple products, competition, and joint inventory and pricing decisions. We also show that using alternative pricing policies that ignore the limited attention of customers or their ability to allocate this attention judiciously can potentially lead to significant profit losses for the firm. We discuss the managerial implications of our key findings and prescribe insights regarding information provision and product positioning.

Equipment manufacturers offer different types of maintenance service plans (MSPs) that delineate payment structures between equipment operators and maintenance service providers. These MSPs allocate risks differently and thus induce different kinds of incentives. A fundamental question, therefore, is how such structures impact service performance and the service chain value. We answer empirically this question. Our study is based on a unique panel data covering the sales and service records of over 700 diagnostic medical body scanners. By exploiting the presence of a standard warranty period, we overcome the key challenge of isolating the incentive effects of MSPs on service performance from the confounding effects of adverse selection. We found that moving an operator from a basic pay-per-service plan to a fixed-fee full-protection plan leads to both a reduction in reliability and an increase in service costs. We further show that the increase in cost is driven by both the operator and the service provider. Our results point to the presence of losses in service chain value in the maintenance of medical equipment, and provide the first evidence that a basic pay-per-service plan, where the risk of equipment failure is borne by the operator, can actually improve performance and costs.

This paper studies the interplay between the operational and financial facets of capacity investment. We consider the capacity choice problem of a firm with limited liquidity and whose access to external capital markets is hampered by moral hazard. The firm must therefore not only calibrate its capacity investment and the corresponding funding needs, but also optimize its sourcing of funds. Importantly, the set of available sources of funds is derived endogenously and includes standard financial claims (debt, equity, etc.). We find that when higher demand realizations are more indicative of high effort, debt financing is optimal for any given capacity level. In this case, the optimal capacity is never below the efficient capacity level but sometimes strictly above that level. Further, the optimal capacity level increases with the moral hazard problem's severity and decreases with the firm's internal funds. This runs counter to the newsvendor logic and to the common intuition that by raising the cost of external capital and hence the unit capacity cost, financial market frictions should lower the optimal capacity level. We trace the value of increasing capacity beyond the efficient level to a bonus effect and a demand elicitation effect. Both stem from the risk of unmet demand, which is characteristic of capacity decisions under uncertainty.

Nonprofit firms sometimes engage in for-profit activities for the purpose of generating revenue to subsidize their mission activities. The organization is then confronted with a consumption vs. investment trade-off, where investment corresponds to providing capacity for revenue customers, and consumption corresponds to serving mission customers. Exemplary of this approach are the Aravind Eye Hospitals in India, where profitable paying hospitals are used to subsidize care at free hospitals. We model this problem as a multi-period stochastic dynamic program. In each period, the organization must decide how much of the current assets should be invested in revenue-customer service capacity, and at what price the service should be sold. We provide sufficient conditions under which the optimal capacity and pricing decisions are of threshold type. Similar results are derived when the selling price is fixed but the banking of assets from one period to the next is allowed. We compare the performance of the optimal threshold policy with heuristics that may be more appealing to managers of nonprofit organizations, and assess the value of banking and of dynamic pricing through numerical experiments.

The immediate motivation of this paper is California Bill AB 394, legislation which mandates fixed nurse-to-patient staffing ratios as a means to address the current crisis in the quality of health care delivery. Modeling medical units as closed queueing systems, we seek to determine whether or not ratio policies are effective at managing nurse workload. Our many-server asymptotic results suggest that ratio policies cannot provide consistently high service quality across medical units of different sizes. As a remedy, we recommend policies that deviate from the restrictive linear nature of ratio policies, employing the 'square root rule' commonly used to staff large service systems. Under some quality of care assumptions, our policies exhibit a type of 'super' pooling effect, in which, for large systems, the requisite workforce is significantly smaller than the nominal patient load.