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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27811
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| Title: | Network-Based Marketing: Identifying Likely Adopters via Consumer Networks |
| Authors: | Hill, Shawndra
Provost, Foster
Volinsky, Chris |
| Keywords: | viral marketing
word of mouth
targeted marketing
network analysis
classification
statistical relational learning |
| Issue Date: | 3-Dec-2008 |
| Series/Report no.: | CeDER-PP-2006-09 |
| Abstract: | Network-based marketing refers to a collection of marketing techniques
that take advantage of links between consumers to increase sales. We
concentrate on the consumer networks formed using direct interactions
(e.g., communications) between consumers. We survey the diverse
literature on such marketing with an emphasis on the statistical methods
used and the data to which these methods have been applied. We also
provide a discussion of challenges and opportunities for this burgeoning
research topic. Our survey highlights a gap in the literature. Because
of inadequate data, prior studies have not been able to provide direct,
statistical support for the hypothesis that network linkage can directly
affect product/service adoption. Using a new data set that represents
the adoption of a new telecommunications service, we show very strong
support for the hypothesis. Specifically, we show three main results:
(1) “Network neighbors”—those consumers linked to a
prior customer—adopt the service at a rate 3–5 times greater
than baseline groups selected by the best practices of the firm’s
marketing team. In addition, analyzing the network allows the firm to
acquire new customers who otherwise would have fallen through the
cracks, because they would not have been identified based on traditional
attributes. (2) Statistical models, built with a very large amount of
geographic, demographic and prior purchase data, are significantly and
substantially improved by including network information. (3) More
detailed network information allows the ranking of the network neighbors
so as to permit the selection of small sets of individuals with very
high probabilities of adoption. |
| URI: | http://hdl.handle.net/2451/27811 |
| Appears in Collections: | CeDER Published Papers
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