Opportunity scoring model for customer relationship management based on supervised machine learning algorithms

Ziyan Zhao; Xinhui Lu; Xin Li; Raelene Huang; Bing Lesley Yuan; Wei Zhang

doi:10.1117/12.2678859

25 May 2023 Opportunity scoring model for customer relationship management based on supervised machine learning algorithms

Ziyan Zhao, Xinhui Lu, Xin Li, Raelene Huang, Bing Lesley Yuan, Wei Zhang

Author Affiliations +

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 127121N (2023) https://doi.org/10.1117/12.2678859
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

The University of Melbourne has gathered data about its past clients for research opportunities that can be used to create a predictive model to estimate the probability of converting a lead to a contract (e.g., research grants, internships, etc). The model can then be used by each faculty in the University to provide business insights for the purposes of improving client engagement, profiling clients and optimizing client relationship processes. This project aims to study which supervised learning algorithms perform well in scoring leads and predicting the probability of success, then to derive further business insights from the best-performing model. A number of machine learning models were developed and the features were tested statistically for their importance. The final results showed that models like random forests and deep neural networks performed relatively well to predict the likelihood of success of each lead. Random forests are less computationally complex and more interpretable than neural networks without sacrificing too much performance, so they may be preferable in this case

Citation Download Citation

Ziyan Zhao, Xinhui Lu, Xin Li, Raelene Huang, Bing Lesley Yuan, and Wei Zhang "Opportunity scoring model for customer relationship management based on supervised machine learning algorithms", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127121N (25 May 2023); https://doi.org/10.1117/12.2678859

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