Predicting Customer Loyalty Using The Internal Transactional Database
G. Verstraeten () and
Dirk Van den Poel ()
Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration
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.
Keywords: Predictive modeling; customer relationship management; behavioral loyalty; overfitting; multicollinearity; data enrichment (search for similar items in EconPapers)
Pages: 39 pages
New Economics Papers: this item is included in nep-bec, nep-cmp, nep-ict and nep-mkt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:rug:rugwps:05/324
Access Statistics for this paper
More papers in Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration Contact information at EDIRC.
Bibliographic data for series maintained by Nathalie Verhaeghe ().