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In hotly contested product categories dominated by a few powerful firms, it is quite common for weaker or late entrants to focus only on particular segments of the whole market. The rationale for such strategy is intuitive: to avoid direct confrontation with heavy-weight firms, and to concentrate in segments where these weaker firms have comparative advantages. In marketing, this is what people called “go niche or go home". The niche-building strategy may rely on “homophily", which implies that consumers in a particular market segment might possess certain set of attributes that cause them to appreciate certain products better (in other words, weaker firms would customize their products to target some particular market segments and not the mass market). On the other hand, the niche-building strategy may also rely on the network effect, which implies that consumers having social relationship would reinforce each other via their respective adoptions. In this case, weaker firms should recognize such inter-customer network and concentrate only on customers belonging to certain set of strategic clusters. In this paper, we present the model for building effective niche-seeking strategies. For simplicity, we assume that the adoption choice depends only on the network effects (in other words, a customer will choose the product that is chosen by the majority of her neighbor). The social network is directed, and there will be two firms, one with significantly more marketing budget than the other firm. Firms take turns making investment choices on which customer to convert. For both firms, their budgets are fixed over time and unused budget will not carry over to future time periods. With this model, we manage to show that a simple strategy based on the evaluation of individual customer's “value" can effectively identify and secure niches within randomly generated scale-free networks. We also show that such niche-building strategy indeed performs better in the long run than a myopic strategy that only cares about immediate market gains.


Computer Sciences | E-Commerce

Research Areas

Intelligent Systems and Decision Analytics


ICEC '12: 14th Annual International Conference on Electronic Commerce, Singapore, 7-8 August 2012

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Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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