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

Year 2020, Volume: 8 Issue: 3, 450 - 455, 30.09.2020

Birol Yüceoğlu

https://doi.org/10.21541/apjes.603809

Cited By: 1

Abstract

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.

Keywords

Customer churn, Machine learning, supervised learning, Length of customer event history, Staying power

Supporting Institution

TÜBİTAK TEYDEB 1501

Project Number

3150376

Thanks

This research was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under TEYDEB 1501 program, under grant numbered 3150376.

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

Abstract

Müşteri terki tahmin problemi çeşitli sektörler için farklı yönlerden incelenmiştir. Bu çalışmada, Türkiye'de önde gelen çevrimiçi hızlı tüketim ürünleri perakendecisinin kayıp tahmin problemi için müşteri olay tarihçesinin uzunluğunun ve öngörücü modellerin kalıcı gücünün etkisini ele alınmaktadır. Bunlar, karar vericilere, müşteri kaybını öngörmek için geçmiş verilerin en uygun uzunluğunu ve öngörücü modellerin ömrünü belirlemede yardımcı olmaları açısından önemli etkilerdir. Çalışma, müşteri olay tarihçesinin uzunluğunun, modellerin kestirimci gücünü  logaritmik olarak artırdığını göstermekte ve literatürde gazete aboneliği sektöründe elde edilen bulguları doğrulamaktadır. Hızlı tüketim sektöründeki müşteri terki modellerin kalıcı gücünün literatürde bir Internet servis sağlayıcısı ve bir sigorta şirketi için belirtilenlerden daha yüksek olduğu görülmektedir.

Keywords

Müşteri terki, yapay öğrenme, gözetimli öğrenme, gözlem süresi uzunluğu, modellerin kalıcı gücü

Project Number

3150376

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