TY - JOUR
T1 - Identification of influencers - Measuring influence in customer networks
AU - Kiss, Christine
AU - Bichler, Martin
PY - 2008/12
Y1 - 2008/12
N2 - Viral marketing refers to marketing techniques that use social networks to produce increases in brand awareness through self-replicating viral diffusion of messages, analogous to the spread of pathological and computer viruses. The idea has successfully been used by marketers to reach a large number of customers rapidly. If data about the customer network is available, centrality measures provide a structural measure that can be used in decision support systems to select influencers and spread viral marketing campaigns in a customer network. Usage stimulation and churn management are examples of DSS applications, where centrality of customers does play a role. The literature on network theory describes a large number of such centrality measures. A critical question is which of these measures is best to select an initial set of customers for a marketing campaign, in order to achieve a maximum dissemination of messages. In this paper, we present the results of computational experiments based on call data from a telecom company to compare different centrality measures for the diffusion of marketing messages. We found a significant lift when using central customers in message diffusion, but also found differences in the various centrality measures depending on the underlying network topology and diffusion process.
AB - Viral marketing refers to marketing techniques that use social networks to produce increases in brand awareness through self-replicating viral diffusion of messages, analogous to the spread of pathological and computer viruses. The idea has successfully been used by marketers to reach a large number of customers rapidly. If data about the customer network is available, centrality measures provide a structural measure that can be used in decision support systems to select influencers and spread viral marketing campaigns in a customer network. Usage stimulation and churn management are examples of DSS applications, where centrality of customers does play a role. The literature on network theory describes a large number of such centrality measures. A critical question is which of these measures is best to select an initial set of customers for a marketing campaign, in order to achieve a maximum dissemination of messages. In this paper, we present the results of computational experiments based on call data from a telecom company to compare different centrality measures for the diffusion of marketing messages. We found a significant lift when using central customers in message diffusion, but also found differences in the various centrality measures depending on the underlying network topology and diffusion process.
KW - Centrality
KW - Customer relationship management
KW - Network theory
KW - Viral marketing
KW - Word of mouth marketing
UR - http://www.scopus.com/inward/record.url?scp=56049120082&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2008.06.007
DO - 10.1016/j.dss.2008.06.007
M3 - Article
AN - SCOPUS:56049120082
SN - 0167-9236
VL - 46
SP - 233
EP - 253
JO - Decision Support Systems
JF - Decision Support Systems
IS - 1
ER -