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Consumers' increasing distrust in traditional advertising (Chalmers, 2017; Lee, 2012) has led them to rely more on reviews from prior buyers connected with them in social networks to learn about new products' characteristics. This consumers' new reliance on social networks has encouraged researchers to i) include consumer-level interactions in social networks to study new products' life-cycle, and ii) exploit information about how consumers are connected in a network to design marketing strategies for new products.
Early literature on diffusion of new products assumed that i) potential adopters were similar and fully connected each other, and ii) pricing strategies did not account for the global structure of connections among potential adopters in a social network.
This research contributes to the literature on diffusion of new products in three ways. First, it develops a new-product diffusion model that captures heterogeneity among potential adopters connected in a social network with density smaller or equal than one. Second, it uses the diffusion model to study the effect of the global structure of connections in the social network on the optimal pricing strategy for a new product.
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Third, it develops an approach to infer the optimal pricing strategy of a new product with approximate Bayesian methods. This research distinguishes between i) a fixed-price strategy and ii) a responsive pricing strategy that updates the new product's price based on its cumulative number of prior adopters.
The new-product diffusion model incorporates potential adopter-specific perceptions about the new product's quality. The approach uses Bayesian methods to update these perceptions with signals from prior buyers connected with them at each time during the diffusion process.
This research illustrates the diffusion model with a dataset from Armelini et al. (2015)'s that concerns the adoption of internet service among households connected in a dynamic social network. Telephone calls among households establish the edges in this network.
To study the effect of the structure of connections in the network on the optimal pricing strategy for a new product, I use agent-based modeling (Rand and Rust, 2011). I simulate the diffusion process with social networks generated with Watts and Strogatz (1998)'s algorithm. For each simulated diffusion process, I study the effect of the network structure on each optimal pricing strategy.
With the internet adoption dataset and with Zheng and Aris-Brosou (2007)'s approach to infer the randomness parameter in Watts and Strogatz's algorithm, this research builds approximate Bayesian posterior distributions of i) the optimal monthly internet fee and ii) the optimal total profit at the end of the study period for each pricing policy. With these distributions, the internet provider may identify months when it overpriced and months when it underpriced its service, relative to the optimal monthly fee.
Diffusion of New Products in Social Networks. (2019, Dec 13). Retrieved from https://studymoose.com/diffusion-of-new-products-in-social-networks-example-essay
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