Using Supervised Machine Learning Methods for RFM Segmentation: A Casino Direct Marketing Communication Case

Abstract

Purpose -This paper explores various supervised machine learning algorithms as an additional classification method to RFM (recency, frequency, and monetary) models with the aim of improving the accuracy in predicting target groups of customers for direct marketing response campaigns conducted by a casino.The purpose of this paper is twofold -first, to test how the addition of demographic variables increases the accuracy of the basic RFM model and second, to assess if and how machine learning algorithms improve the initial model.Ultimately, we propose a model for direct marketing response at individual level using RFM scores and customer demographic and behavioral data as endogenous variables to be used by the company.The findings can be used as an alternative to the simpler RFM model when approaching customer response modeling for large datasets and can be generalized to other industries.Design/Methodology/Approach -Our research employed supervised machine learning methods tuned on historical responses to a casino's direct marketing activities to improve the company's RFM segmentation model. SažetakSvrha -Rad istražuje različite nadzirane algoritme strojnoga učenja kao dodatne metode klasifikacije RFM-a (recentnost, učestalost i monetarnost) modelima da bi se poboljšala točnost predviđanja ciljanih skupina potrošača za kampanje izravne marketinške komunikacije za kasino.Svrha je rada dvostruka.Prvo, ispitati kako dodavanje demografskih varijabli povećava točnost osnovnog RFM modela i, drugo, procijeniti poboljšavaju li i kako algoritmi strojnog učenja početni model.U konačnici, predlažemo model za izravni marketinški odgovor na individualnoj razini koristeći RFM rezultate te demografske i podatke o ponašanju potrošača kao endogene varijable za korištenje od strane poduzeća.Rezultati se mogu koristiti za pristup modeliranju potrošačkih odgovora za velike skupove podataka i kao alternativa jednostavnijem RFM modelu te se mogu generalizirati na druge industrije.Metodološki pristup -Istraživanje koristi nadzirane metode strojnoga učenja usklađene s povijesnim odgovorima na aktivnosti izravne marketinške komunikacije kasina kako bi se poboljšao RFM segmentacijski model Danijel Bratina, Armand Faganel 8

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 6 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.