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).

This work is partially financed by national funds through FCT – Fundação para a Ciência e a Tecnologia under the project UIDB/00006/2020. https://doi.org/10.54499/UIDB/00006/2020.

Telecommunications,decision maker,machine learning,random forest,pre-diction