The impact of machine learning on the efficiency of the B2B sales service in pharmaceutical companies

The explanatory study examines the possible value of Machine Learning in the B2B sales process in pharmaceutical companies. Sales representatives accounting for a wide range of activities, suffering from time consuming and repetitive tasks. This study investigates the potential of Machine Learning applications for B2B sales in order to facilitate sales representative’s daily tasks and enhance the entire sales process. The results have been obtained through qualitative research based on 8 interviews with AI-experts, pharma consultants and sales representatives as well as secondary data in form of academic articles and reports. The findings reveal that, compared to other departments, ML-applications in B2B sales are less applied at the current stage, but mostly in the customer service process. The interviews have shown that the usage of ML-applications is possible within all steps of the sales process and enhances its overall efficiency and effectivity in terms of time, costs and quality. Furthermore, tasks which increase the efficiency of the sales department through ML applications are outlined. By applying ML within the B2B sales process, the daily work of sales representatives can be facilitated, which ultimately could not only have a positive impact on customer satisfaction, but also on employee commitment leading to competitive advantage in the price intense environment of the pharmaceutical industry.O presente estudo foca-se na possível importância da Aprendizagem Automática no serviço de vendas B2B em Empresas Farmacêuticas. Representantes de vendas responsáveis por uma grande variedade de actividades, afectados pelas demoradas e longas tarefas. Esta dissertação examina o potêncial da Aprendizagem Automática nas vendas B2B a fim de facilitar as tarefas diárias dos representantes de vendas, e de melhorar ainda todo o processo de vendas. Os resultados são obtidos através de uma pesquisa qualitativa baseada em 10 entrevistas com AI-experts, consultantes farmacêuticos e representantes de vendas, assim como fichas de dados provenientes de artigos e relatórios. Os resultados revelam que, em comparação com outros departamentos, a aplicação da Aprendizagem Automática em vendas B2B são actualmente menos aplicadas, sobretudo no que diz respeito ao atendimento ao cliente. As entrevistas mostraram que o uso da Aprendizagem Automática é possível em todas as fases do processo de vendas sendo que melhora toda a sua eficiência e efetividade em termos de tempo, custos e qualidade. Posteriormente, as tarefas de vendas mais eficientes dentro das farmácias estão estabelecidas; pelo que, a introdução da Aprendizagem Automática dentro do processo de vendas B2B poderá facilitar e, inclusive, melhorar o trabalho dos representantes de vendas, sendo que esta otimazação poderá, por conseguinte, não só ter um impacto positivo na satisfação do cliente como também no compromisso dos empregados originando, desta forma, uma vantagem competitiva face ao intenso ambiente de preços na industria farmacêutica

The use of machine learning algorithms in recommender systems: A systematic review

The final publication is available at Elsevier via https://doi.org/10.1016/j.eswa.2017.12.020 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics.Natural Sciences and Engineering Research Council of Canada (NSERC) Ontario Research Fund of the Ontario Ministry of Research, Innovation, and Scienc