CRM Market Overview: A Case Study of Job Vacancies - Carmo, Fernando Almeida Do; Menezes, Pedro Henrique Costa; Barata, Bárbara Adriana Pires; Jacob, Antonio Fernando Lavareda; Lobato, Fábio Manoel França (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI), 2024)
Context: Social media has revolutionized how companies relate to their customers, directly impacting Customer Relationship Management (CRM) systems. Furthermore, such media have drastically changed how companies advertise job vacancies and recruit employees. Problem: Considering the recruitment platforms multitude and the increased job post data volume, extracting knowledge to keep up with market developments represents a prominent research challenge. Solution: We present a textual analysis of job advertisements related to CRM using text mining methods and a lexical construction. We identified the most common vacancies, as well as knowledge, technical skills (hard skills), and interpersonal skills (soft skills). IS Theory: This work was conceived from the perspective of Design Theory, mainly concerning the construction of artifacts that allow textual analysis. Method: The analyses were carried out using a methodology based on Design Science Research, where the problem was identified, the objectives were outlined, and moving on to the design stage, where we applied text mining techniques including N-gram analysis of job descriptions, topic modeling and the lexicon construction oriented to the CRM area. Summary of Results: The research findings reveal the main knowledge areas, experience requirements, skills, training, and technologies that stand out in this sector, as well as characteristics such as preference for diverse teams, concerned with gender equity, inclusion of LGBQIAP+ community and race/color diversity. The constructed lexicon can be used for a more precise and structured view of the CRM employment scenario. Contributions and Impact in the IS area: This information provides good opportunities for candidates and recruiters in the hiring process, allowing both to identify such aspects more precisely and supporting decision-making.