Refining B2B CRM Systems Customer Data Using Machine Learning - Almonkari, Julle (2025)
This research explores the use of machine learning (ML) in refining customer data obtained through customer relationship management (CRM) systems. The objective is to provide insights on how ML, regarding CRM systems and customer data, is currently utilized together with how it could and should be utilized in the future. The topic's relevancy is very high due to the increasing focus on utilizing ML in CRM systems as well as other business functions. Feedback from interviewees indicates that there are ways it should be improved. The research was conducted utilizing a qualitative approach, combining a literature review as well as semi-structured interviews which were analyzed using a content analysis approach. The literature review material was chosen based on its relevancy to the research topic while acknowledging the rapidly evolving nature of the ML field, thus aiming to use more current publications. Interviews were conducted with a limited number of participants (5) with the focus being on their subject expertise. The research results gave way to some of the following discoveries: understanding that factors such as consistency and churn prediction were seen as enabling the B2B sales process to advance. Supervised (like classification models) and unsupervised learning (like clustering algorithms) were seen as the best for guiding B2B. Research views on ML in CRM systems showed that the focus is on churn prediction while understanding the possibilities with DL. Data in CRM systems is used for process management, fundraising and lead scoring.