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Oppose to the strategy of maximizing the spread of commercial advertising though/within the members of a social network, the key issue for the most firms in brand marketing should be targeting the set of individuals who may consume with higher possibility under the inevitable budget constraint. In this research we introduce a prediction framework for “precision targeting” based on the concepts of information cascades and the effects of the consumer conformity. That is, the goal of this framework is to investigate the processes to detect the influencers with higher purchasing possibility in the coming time period. Given the difficulties of data collecting and analyzing, this so called “Social Influence Tagging” can thus be as a service for all the brands for marketing strategy.

This paper shows that a selective piracy-detection strategy for the monopolist is rational in a reproducible software market. Different detection strategies and the corresponding price/penalty strategies adopted by the monopolist, with the considerations of detection costs and network externality, are demonstrated in a simplified two-period model. Under some specific circumstances, the software monopolist could gain a higher profit if not detecting piracy (closing one eye) at the beginning but detecting it (opening the other eye) later. In short, detection strategies would be time-inconsistent. Moreover, from the social planner's perspective, the monopolist's best detection strategy is not always socially optimal; that is, not-enforcing copyright protection may be privately and socially beneficial.