Who would have guessed that a plastic clog shoe which seemed to be trapped in the realm of generic boat shoe would become a selling phenomenon grossing over 200 million dollars in the first quarter of 2008. How did this happen exactly? Traditional market research could never have predicted such an event occurring and has been unable in the past to predict the rise of products whose popularity seems to increase exponentially overnight. The answer to how this happened is drawn from the concept discussed in class of network externalities, specifically information cascades. In the past market research has only been able to make conclusions as to why an item such as Crocs became as popular as they did in hindsight. However, new methods known as “agent-based simulations” hopes to predict not how to make a product take off but in what conditions would a product takeoff and when would the product be impacted by a cluster of density which would prevent the proliferation of the popularity of that item.

The running of such a simulation involves placing all the known values and effects an item may have and looping through the simulation program changing certain variables. The variables include the demographics of the initial clients, geographic considerations, pricing schemes and other common marketing factors. Under certain conditions the popularity of an item will spread beyond its initial borders and cause the mass popularity seen in numerous “fad” items. Two reasons why this agent-based simulation may be fruitful in their applications to marketing are one, the adoption process unfolds over time versus traditional scenario based models and two the interactions between consumers and manufacturers affect the rate of implementation.

The ability to predict market conditions conducive to the adoption of a product such as Crocs would be the next step in marketing and possibly predicting and causing the implementation of such a cascade. This shows potential for the incorporation of network effects on consumer product marketing teams.

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