English, Article, Journal or magazine article edition: Predicting Customer Loyalty Using The Internal Transactional Database W. BUCKINX; G. VERSTRAETEN; D. VAN DEN POEL

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Bookmark: http://trove.nla.gov.au/version/15318
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Predicting Customer Loyalty Using The Internal Transactional Database
Author
  • W. BUCKINX
  • G. VERSTRAETEN
  • D. VAN DEN POEL
Physical Description
  • preprint
Notes
  • Loyalty and targeting are central topics in Customer Relationship Management. Yet, the information that resides in customer databases only records transactions at a single company, whereby customer loyalty is generally unavailable. In this study, we enrich the customer database with a prediction of a customer's behavioral loyalty such that it can be deployed for targeted marketing actions without the necessity to measure the loyalty of every single customer. To this end, we compare multiple linear regression with two state-of-the-art machine learning techniques (random forests and automatic relevance determination neural networks), and we show that (i) a customer’s behavioral loyalty can be predicted to a reasonable degree using the transactional database, (ii) given that overfitting is controlled for by the variable-selection procedure we propose in this study, a multiple linear regression model significantly outperforms the other models, (iii) the proposed variable-selection procedure has a beneficial impact on the reduction of multicollinearity, and (iv) the most important indicator of behavioral loyalty consists of the variety of products previously purchased.
  • Predictive modeling; customer relationship management; behavioral loyalty; overfitting; multicollinearity; data enrichment
  • RePEc:rug:rugwps:05/​324
Language
  • English
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