Inkrementell responsanalys: Vilka kunder bör väljas vid riktad marknadsföring?
Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Incremental response analysis : Which customers should be selected in direct marketing? (English)
If customers respond differently to a campaign, it is worthwhile to find those customers who respond most positively and direct the campaign towards them. This can be done by using so called incremental response analysis where respondents from a campaign are compared with respondents from a control group. Customers with the highest increased response from the campaign will be selected and thus may increase the company’s return. Incremental response analysis is applied to the mobile operator Tres historical data. The thesis intends to investigate which method that best explain the incremental response, namely to find those customers who give the highest incremental response of Tres customers, and what characteristics that are important.The analysis is based on various classification methods such as logistic regression, Lassoregression and decision trees. RMSE which is the root mean square error of the deviation between observed and predicted incremental response, is used to measure the incremental response prediction error. The classification methods are evaluated by Hosmer-Lemeshow test and AUC (Area Under the Curve). Bayesian logistic regression is also used to examine the uncertainty in the parameter estimates.The Lasso regression performs best compared to the decision tree, the ordinary logistic regression and the Bayesian logistic regression seen to the predicted incremental response. Variables that significantly affect the incremental response according to Lasso regression are age and how long the customer had their subscription.
Place, publisher, year, edition, pages
2013. , 39 p.
Incremental response modeling, uplift modeling, database marketing, Net information value, Lasso regression, Bayesian logistic regression, decision trees, logistic regression
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-96593ISRN: LIU-IDA/STAT-G--13/004—SEOAI: oai:DiVA.org:liu-96593DiVA: diva2:642346
Subject / course
Program in Statistics and Data Analysis
Wegmann, Bertil, Post Doc
Wahlin, Karl, Ph. D.