Salespeople performance evaluation with predictive analytics in B2B

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 modelinfo:eu-repo/semantics/publishedVersio

IPO Ready? Illuminating the Dark Box of Private Equity

The use of public equity data can help combat the challenges private equity funds currently face regarding data availability. The goal is to create a model to provide guidance to both investors and entrepreneurs in the decision-making process. The data gathered would provide insight on how close a private company is to a successful Initial Public Offering (IPO). The idea is that a model, showing the average financial metrics of companies within certain industries during an IPO, can provide new perceptiveness as to how the private company is performing

Constituent Elements for Prescriptive Analytics Systems

Prescriptive analytics has emerged as a technological driver in data-intensive enterprise environ- ments, as it tries to transform valuable insights into actionable recommendations and act upon them in order to meet business objectives. The basic idea is to go beyond the findings of descriptive data anal- ysis and predictive modeling to answer the questions “What should be done?” and “Why should it be done?”. However, there is often an inconsistent understanding about constituent elements of prescrip- tive analytics, which may hinder the development of adequate information systems. For this reason, the paper deals with a conceptualization by conducting a systematic literature review. The research goal is to extract fundamental aspects and facets from different perspectives and consolidate them into a coherent view towards a common understanding of a prescriptive analytics system

Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Mastering the digital transformation of sales

Managerial and academic literature provide only limited guidance on how to drive the digital transformation of sales. This article presents a model for in-depth analysis of sales processes, goals for each process in terms of effectiveness and efficiency, and a structured set of digital responses. For managers, it provides actionable guidelines on how to drive the digital transformation of sales, a large set of inspiring examples, and an international benchmarking opportunity

Predictive analysis for sales: A B2B case

Measuring salespeople’s performance 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, 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 author applied a Naive Bayes model to classify salespeople into pre-defined categories provided by the business, through the use of data mining techniques over a dataset of about three years of sales made by 566 salespeople of a global freight forwarder. 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 baseline, target achievement among others. The author also identified the most critical factors for salesperson’s success based on the dataset as Growth amount, Target achievement, Growth percentage, and the number of months with growth above 0. The author 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 92,10% for the whole model.Avaliar a performance de vendedores é um processo que ocorre várias vezes por ano numa empresa. Durante este processo, o gestor e o vendedor avaliam o desempenho do vendedor em vários Indicadores de Performance. Para a reunião de avaliação, os gestores recolhem dados do sistema de Gestão de Vendas, Sistemas Financeiros, ficheiros Excel, entre outros, levando a um processo longo e exaustivo. O resultado da avaliação de desempenho é uma classificação seguida por sugestões de melhoria. Atualmente, através das tecnologias de análise preditiva, é possível fazer classificações com base em dados. Neste trabalho, o autor aplicou um modelo Naive Bayes para classificar os vendedores em categorias predefinidas fornecidas pelo negócio, usando técnicas de data mining aplicados a um conjunto de dados, composto por cerca de três anos de vendas de um transitário global. A classificação é feita em 3 classes, sendo estas: Baixo desempenho, Bom e Fora de Série, a classificação foi alcançada com base em KPI’s como a percentagem de crescimento, a variabilidade de vendas entre muitos outros. O autor também identificou os fatores críticos para o sucesso de um vendedor, de acordo com os dados, como sendo volume do crescimento da base de clientes, a capacidade de atingir os objetivos, a percentagem de crescimento e número de meses com crescimento positivo. O autor avaliou o desempenho do modelo com uma matriz de confusão e outras técnicas como True Positives, Negatives, e o score F1. Os resultados apresentaram uma precisão de 92,10 % para todo o modelo

Algorithmic Fairness in Business Analytics: Directions for Research and Practice

The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies. However, these advances have simultaneously drawn attention to rising legal and ethical challenges when BA inform decisions with fairness implications. As a response to these concerns, the emerging study of algorithmic fairness deals with algorithmic outputs that may result in disparate outcomes or other forms of injustices for subgroups of the population, especially those who have been historically marginalized. Fairness is relevant on the basis of legal compliance, social responsibility, and utility; if not adequately and systematically addressed, unfair BA systems may lead to societal harms and may also threaten an organization's own survival, its competitiveness, and overall performance. This paper offers a forward-looking, BA-focused review of algorithmic fairness. We first review the state-of-the-art research on sources and measures of bias, as well as bias mitigation algorithms. We then provide a detailed discussion of the utility-fairness relationship, emphasizing that the frequent assumption of a trade-off between these two constructs is often mistaken or short-sighted. Finally, we chart a path forward by identifying opportunities for business scholars to address impactful, open challenges that are key to the effective and responsible deployment of BA