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Margarida P. Dias, Ramos, F.R., João J. Ferreira Gomes, Susana C. Almeida & Rita N. Dias (2025). Decision Maker Contact Prediction Model in a Business Context: A Machine Learning Approach. In  Human-Centred Technology Management for a Sustainable Future.: Springer Nature.

M. P. Dias et al.,  "Decision Maker Contact Prediction Model in a Business Context: A Machine Learning Approach", in  Human-Centred Technology Management for a Sustainable Future, Springer Nature, 2025

@incollection{dias2025_1766312625425,
	author = "Margarida P. Dias and Ramos, F.R. and João J. Ferreira Gomes and Susana C. Almeida and Rita N. Dias",
	title = "Decision Maker Contact Prediction Model in a Business Context: A Machine Learning Approach",
	chapter = "",
	booktitle = " Human-Centred Technology Management for a Sustainable Future",
	year = "2025",
	volume = "",
	series = "",
	edition = "",
	publisher = "Springer Nature",
	address = "",
	url = "http://dx.doi.org/10.1007/978-3-031-72494-7_19"
}

TY  - CHAP
TI  - Decision Maker Contact Prediction Model in a Business Context: A Machine Learning Approach
T2  -  Human-Centred Technology Management for a Sustainable Future
AU  - Margarida P. Dias
AU  - Ramos, F.R.
AU  - João J. Ferreira Gomes
AU  - Susana C. Almeida
AU  - Rita N. Dias
PY  - 2025
DO  - 10.1007/978-3-031-72494-7_19
UR  - http://dx.doi.org/10.1007/978-3-031-72494-7_19
AB  - In the business-to-business sector of a telecommunications company, each company/customer has several contacts associated with its portfolio. The challenge is to identify the critical contact. In this context, by combining human skills with the strengths of technology, it is possible to gain insights that support the management process and business efficiency. The main objective of this study is to develop a predictive model that estimates the likelihood that a contact is a customer decision maker. A binary response variable was created and four formulations were tested using commercial outcome data. A machine learning algorithm (Random Forest) with Bayesian hyperparameter optimisation was used to identify the case that gave the best results. The results were validated through telemarketing campaigns. The developed model successfully overcame the challenge of identifying the critical contact. The support provided by the technology thus proved to be an asset for the telecommunications company (guaranteeing efficiency gains and a higher decision rate).
ER  -