# Salespeople Performance Evaluation with Predictive Analytics in B2B
## 1 Introduction  
Salesperson performance measurement is a process that occurs multiple times per year on a company. The performance evaluation is based on various Key Performance Indicators (KPI's) extracted from multiple systems like Customer Relationship Management (CRM), and Enterprise Resource Planning (ERP). Evaluating these KPI's can be time-consuming as they require the analysis of figures with complex calculations, a judgment based on the values, and the weight that each of the KPI's contributes to the performance as a whole. The KPI's often include the amount of products/services sold by the salesperson, the number of opportunities created, the ability to sell multiple products/services, the variability of the sales along the year, among many others. When a company has dozens or hundreds of salespeople, this process transforms on a thorough process that may involve other departments like Human Resources (HR) and Operations. The result of the performance evaluation is a classification followed by actions to improve the performance where it is needed. Technology, through Data Mining (DM), currently is capable of make classification based on data. DM is the process of exploration and analysis, by automatic or semiautomatic means, of large quantities of data to discover meaningful patterns and rules [1]. DM tasks are classified into two categories: descriptive and predictive [1]. The predictive tasks, are theones that perform inferences based on data to make predictions. The goal of these tasks is to create a predictive model. The goal of the predictive model is to allow the data miner to predict an unknown value of a specific variable. When the result of the prediction is a number, it is called a regression, and when the result is a label it's called a classification [1]. DM classification capabilities can help improving the process of the salesperson performance measurement. Companies can take advantage of the Predictive Analytics (PA) classification capabilities, to help on the judgment of KPI's that are based on complex calculations and the weight that each KPI contributes to the whole performance evaluation. By using classifications previously made by humans, companies can build models that can classify current sales of a salesperson and use them on the performance evaluation. Through these models it is possible to automate part of the performance evaluation process. The gains these automated evaluations can bring to the companies are among others:  
* Reduction on the number of hours needed to analyse multiple KPI's to make a judgement of the salesperson performance, allowing the managers and salesperson to focus on other tasks that bring value to the business
* Improve the Salesperson Performance Appraisal process, by providing in advance, the possible future evaluation of the performance based on the salesperson current sales
* Allow the salesperson to act sooner in the performance measurement and appraisal time
* By allowing the salesperson to act sooner, companies can face reductions in salespeople turnover, and consequently reductions of costs on recruiting and training  
In this work, the authors propose the use of DM techniques, to allow salesperson and sales leaders to make a better decision about salespeople performance measurement, by building a model in R that can classify a salesperson's performance based on metrics defined by the business. As many companies can have different evaluation processes, all companies in B2B area that has teams of salespeople being measured based on metrics, can take advantage of this DM process. The dataset used for this analysis is composed of data regarding salespeople performance measurement from a Freight Forwarding global company. The sales are made by 594 salespeople between January 2017 and June 2019. This measurement is based on the company's internal performance measurement process, that are explained in this article on a very high level, to provide to the reader an understanding of the data, and the fields necessary to make the performance measurement.