Abstract
The rise of online social networks has been the most significant development on the Internet in the last decade. It has not only transformed how consumers interact with each other, but also affected the way companies communicate with their customers. This development has also offered a wealth of data to better understand social interactions between consumers and to optimize marketing strategies in the presence of social influence. In this chapter, the authors provide a generalized framework to integrate social network data into traditional marketing decision models. The authors show how their framework nests several existing approaches to deal with social network data. They also discuss the empirical challenges researchers may encounter and possible solutions.
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Notes
- 1.
See http://www.emarketer.com/Articles/Results?t=1045&p=5 for more details.
- 2.
Structural equivalence refers to the extent to which two consumers are connected to the same or similar others. One frequently used formula is: \(SE_{ij} = \frac{{d_{ij} }}{{d_{i} + d_{j} - d_{ij} }}\), where \(d_{ij} = \sum\nolimits_{k \ne i,j} {a_{ik} a_{jk} }\).
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Chen, X., van der Lans, R., Trusov, M. (2017). Integrating Social Networks into Marketing Decision Models. In: Wierenga, B., van der Lans, R. (eds) Handbook of Marketing Decision Models. International Series in Operations Research & Management Science, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-56941-3_17
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