@article{article_603809, title={The Effect of the Length of the Customer Event History and the Staying Power of the Predictive Models in the Customer Churn Prediction: Case Study of Migros Sanal Market}, journal={Academic Platform - Journal of Engineering and Science}, volume={8}, pages={450–455}, year={2020}, DOI={10.21541/apjes.603809}, author={Yüceoğlu, Birol}, keywords={Customer churn, Machine learning, supervised learning, Length of customer event history, Staying power}, abstract={<p class="MsoNormal" style="line-height:normal;"> <span lang="en-us" xml:lang="en-us">The customer churn
prediction problem is studied for various sectors under several aspects. In
this study, we consider the effect of the length of the customer event history
and the staying power of the predictive models for the churn prediction problem
of a leading online fast-moving consumer goods retailer in Turkey. These are
important aspects of the churn prediction models as they help decision makers
to determine the optimal length of the past data for predicting the customer
churn as well as lifespan of the predictive models. We find that the length of
the customer event history logarithmically increases the predictive power of
models, validating findings in the literature in the newspaper subscription
sector. Regarding the staying power of the predictive models, we conclude that
the models in online fast-moving consumer goods retailing has a slightly longer
lifespan that the models discussed in the literature for an Internet service
provider and an insurance company. </span> </p> <p> </p>}, number={3}, publisher={Akademik Perspektif Derneği}, organization={TÜBİTAK TEYDEB 1501}