Ralf van der Lans, G. V. van Bruggen, J. Eliashberg, B. Wierenga
{"title":"Seeding a Message to Harvest Reach. Predicting and Optimizing the Spread of Electronic Word-of-Mouth","authors":"Ralf van der Lans, G. V. van Bruggen, J. Eliashberg, B. Wierenga","doi":"10.2478/GFKMIR-2014-0039","DOIUrl":null,"url":null,"abstract":"Abstract In a viral marketing campaign organizations stimulate customers to forward marketing messages to their contacts. To optimize a viral campaign it is necessary to predict how many customers will be reached, how this reach evolves, and how it depends on promotion activities. a new Viral Branching model can provide these results. It is based on insights from epidemiology and the spread of viruses and was adapted to a marketing context and an electronic environment. The model is applied to an actual viral marketing campaign in which over 200,000 customers participated during a six-week period. The results show that the model quickly predicts the actual reach of the campaign and serves as a valuable tool to support marketing decisions related to online campaigns","PeriodicalId":30678,"journal":{"name":"GfK Marketing Intelligence Review","volume":"21 1","pages":"32 - 41"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GfK Marketing Intelligence Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/GFKMIR-2014-0039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Abstract In a viral marketing campaign organizations stimulate customers to forward marketing messages to their contacts. To optimize a viral campaign it is necessary to predict how many customers will be reached, how this reach evolves, and how it depends on promotion activities. a new Viral Branching model can provide these results. It is based on insights from epidemiology and the spread of viruses and was adapted to a marketing context and an electronic environment. The model is applied to an actual viral marketing campaign in which over 200,000 customers participated during a six-week period. The results show that the model quickly predicts the actual reach of the campaign and serves as a valuable tool to support marketing decisions related to online campaigns
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