The usefulness of a software product becomes obvious to consumers only after they get to experience it and, upon experiencing it, they may reach different conclusions regarding its true value. We examine the problem of designing free software trials under a general learning function. Our analyses lead to several new findings. We find that a time-locked trial is optimal only when the rate of learning is sufficiently large. It is not optimal in other situations, even when it has an overall positive effect on consumers' valuations. We also find that positive network effects have a minimal impact on this optimality. Interestingly, we find that neither the optimal trial period nor the optimal price is monotonically increasing in the rate of learning. At moderate rates, the software manufacturer pursues a dual strategy of offering a longer trial as well as a lower price. At higher rates of learning, the manufacturer does the opposite. Our results are robust, and incorporating possibilities such as a trial providing a signal of quality or learning being correlated with prior valuation has little impact on their applicability.
We consider advertising problems under an information technology (IT) capacity constraint encountered by electronic retailers in a duopolistic setting. There is a considerable amount of literature on advertising games between firms, yet introducing an IT capacity constraint fundamentally changes this problem. In the presence of information processing constraints, although advertising may still cause a customer to switch, it may not result in a sale, i.e., the customer may be lost by both firms. This situation could occur when customers have a limited tolerance for processing delays and leave the website of a firm because of slow response. In such situations, attracting more traffic to a firm's site (by increasing advertising expenditure) may not generate enough additional revenue to warrant this expenditure. We use a differential game formulation to obtain closedform solutions for the advertising effort over time in the presence of IT capacity constraints. Based on these solutions, we present several useful managerial insights.
One of the distinctive features of sites on the Internet is their ability to gather enormous amounts of information about their visitors and to use this information to enhance a visitor's experience by providing personalized information or recommendations. In providing personalized services, a website is typically faced with the following trade-off: When serving a visitor's request, it can deliver an optimally personalized version of the content to the visitor, possibly with a long delay because of the computational effort needed, or it can deliver a suboptimal version of the content more quickly. This problem becomes more complex when several requests are waiting for information from a server. The website then needs to trade off the benefit from providing more personalized content to each user with the negative externalities associated with higher waiting costs for all other visitors that have requests pending. We examine several deterministic resource allocation policies in such personalization contexts. We identify an optimal policy for the above problem when requests to be scheduled are batched, and show that the policy can be very efficiently implemented in practice. We provide an experimental approach to determine optimal batch lengths, and demonstrate that it performs favorably when compared with viable queueing approaches.