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Calixto, N. & Ferreira, J. (2020). Salespeople performance evaluation with predictive analytics in B2B. Applied Sciences. 10 (11), 1-24

N. Calixto and J. C. Ferreira,  "Salespeople performance evaluation with predictive analytics in B2B", in Applied Sciences, vol. 10, no. 11, pp. 1-24, 2020

@article{calixto2020_1715078363336,
	author = "Calixto, N. and Ferreira, J.",
	title = "Salespeople performance evaluation with predictive analytics in B2B",
	journal = "Applied Sciences",
	year = "2020",
	volume = "10",
	number = "11",
	doi = "10.3390/app10114036",
	pages = "1-24",
	url = "https://www.mdpi.com/2076-3417/10/11/4036"
}

TY  - JOUR
TI  - Salespeople performance evaluation with predictive analytics in B2B
T2  - Applied Sciences
VL  - 10
IS  - 11
AU  - Calixto, N.
AU  - Ferreira, J.
PY  - 2020
SP  - 1-24
SN  - 2076-3417
DO  - 10.3390/app10114036
UR  - https://www.mdpi.com/2076-3417/10/11/4036
AB  - Performance Evaluation is a process that occurs multiple times per year on a company. During this process, the manager and the salesperson evaluate how the salesperson performed on numerous Key Performance Indicators (KPIs). To prepare the evaluation meeting, managers have to gather data from Customer Relationship Management System, Financial Systems, Excel files, among others, leading to a very time-consuming process. The result of the Performance Evaluation is a classification followed by actions to improve the performance where it is needed. Nowadays, through predictive analytics technologies, it is possible to make classifications based on data. In this work, the authors applied a Naive Bayes model over a dataset that is composed by sales from 594 salespeople along 3 years from a global freight forwarding company, to classify salespeople into pre-defined categories provided by the business. The classification is done in 3 classes, being: Not Performing, Good, and Outstanding. The classification was achieved based on KPI’s like growth volume and percentage, sales variability along the year, opportunities created, customer base line, target achievement among others. The authors assessed the performance of the model with a confusion matrix and other techniques like True Positives, True Negatives, and F1 score. The results showed an accuracy of 92.50% for the whole model
ER  -