A Method for Simultaneous Production and Order Planning in a Cooperative Supply Chain Relationship with Flexibility Contracts

Rust, Tobias; Brüggemann, Daniel; Dangelmaier, Wilhelm; Picker-Huchzermeyer, Dominik

doi:10.1109/hicss.2010.17

Cited by 1 publication

(1 citation statement)

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“…These algorithms allow for a more efficient evaluation of these data to predict the results of the marketing campaigns, or even the complete automation of the operation (Moro et al 2014). The forecasts allow, in the majority of cases, an operational adjustment to build longer and closer relationships, according to the demand of the companies (Rust et al 2010).…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework towards Bank Telemarketing Prediction

Tékouabou

Gherghina

Toulni

et al. 2022

JRFM

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The use of machine learning (ML) methods has been widely discussed for over a decade. The search for the optimal model is still a challenge that researchers seek to address. Despite advances in current work that surpass the limitations of previous ones, research still faces new challenges in every field. For the automatic targeting of customers in a banking telemarketing campaign, the use of ML-based approaches in previous work has not been able to show transparency in the processing of heterogeneous data, achieve optimal performance or use minimal resources. In this paper, we introduce a class membership-based (CMB) classifier which is a transparent approach well adapted to heterogeneous data that exploits nominal variables in the decision function. These dummy variables are often either suppressed or coded in an arbitrary way in most works without really evaluating their impact on the final performance of the models. In many cases, their coding either favours or disfavours the learning model performance without necessarily reflecting reality, which leads to over-fitting or decreased performance. In this work, we applied the CMB approach to data from a bank telemarketing campaign to build an optimal model for predicting potential customers before launching a campaign. The results obtained suggest that the CMB approach can predict the success of future prospecting more accurately than previous work. Furthermore, in addition to its better performance in terms of accuracy (97.3%), the model also gives a very close score for the AUC (95.9%), showing its stability, which would be very unfavourable to over-fitting.

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Section: Introductionmentioning

confidence: 99%