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Customer calls serve as a valuable source of feedback for financial service providers, potentially containing a wealth of unexplored insights into customer questions and concerns. However, these call data are typically unstructured and challenging to analyze effectively. This thesis project focuses on leveraging Topic Modeling techniques, a sub-field of Natural Language Processing, to extract meaningful customer insights from recorded customer calls to a European financial service provider. The objective of the study is to compare two widely used Topic Modeling algorithms, Latent Dirichlet Allocation (LDA) and BERTopic, in order to categorize and analyze the content of the calls. By leveraging the power of these algorithms, the thesis aims to provide the company with a comprehensive understanding of customer needs, preferences, and concerns, ultimately facilitating more effective decision-making processes. 

Through a literature review and dataset analysis, i.e., pre-processing to ensure data quality and consistency, the two algorithms, LDA and BERTopic, are applied to extract latent topics. The performance is then evaluated using quantitative and qualitative measures, i.e., perplexity and coherence scores as well as in- terpretability and usefulness of topic quality. The findings contribute to knowledge on Topic Modeling for customer insights and enable the company to improve customer engagement, satisfaction and tailor their customer strategies. 

The results show that LDA outperforms BERTopic in terms of topic quality and business value. Although BERTopic demonstrates a slightly better quantitative performance, LDA aligns much better with human interpretation, indicating a stronger ability to capture meaningful and coherent topics within company’s customer call data.