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Norwegian Open Research Archives
Master thesis . 2020
Data sources: Norwegian Open Research Archives
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BI Open
Master thesis . 2020
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Comparison of classical RFM models and Machine learning models in CLV prediction

descriptionPublicationkeyboard_double_arrow_right Master thesis 01 Jan 2020 Norway English Publisher:Handelshøyskolen BI
Authors: Qismat, Temor; Feng, Yan;

handle: 11250/2688689

Comparison of classical RFM models and Machine learning models in CLV prediction

Abstract

This study analyzes the difference between classical RFM models and Machine Leaning (ML) models when calculating CLV with transactional data. In this paper we run different programs to analyze the CLV value by using both methods. Based on the results, the researchers found out that Pareto/NBD model have better predictive power of performing CLV predictions than ML models. Lastly, the findings proved the effectiveness of the Pareto/NBD method of calculating CLV.

Masteroppgave(MSc) in Master of Science in Business Analytics - Handelshøyskolen BI, 2020

Country
Norway
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Keywords

business analytics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green