Leveraging artificial intelligence in SaaS customer relationship management : A qualitative content analysis of customer stories from CRM system vendors

The increased competition resulting from globalisation has led to a need to respond to customer requirements more efficiently. SaaS companies are constantly competing with new market entrants, which has also increased the need for internationalisation. Cloud-based SaaS companies have access to a large amount of customer data, which can be utilised to enhance customer relationship management. The ability to leverage growing customer data significantly determines a company's capacity for effective customer relationship management. By integrating artificial intelligence applications into customer relationship management systems, SaaS companies have the opportunity to accelerate and personalise communication with customers, identify key customers, and optimise their own processes to prevent churn, increase revenue, and succeed in international competition. The potential of AI for SaaS companies is vast, but the variety of ways to utilise it has created a challenge for consistent implementation. At the same time, previous scientific research has become fragmented, focusing on different functionalities and effects of AI, leaving less attention to the differences across various industries in the potential utilisation of AI in customer relationship management. This thesis examines the typical goals and challenges of customer relationship management in SaaS companies, as well as existing AI functionalities as solutions. The literature review consolidates common features related to modern customer relationship management, AI-enhanced functionalities, and key goals and challenges for customer relationship management in SaaS companies. The empirical part of the study was conducted as a qualitative content analysis using secondary, publicly available data. The data consisted of customer stories from seven different companies offering AI-enhanced customer relationship management systems, with SaaS companies as clients. A total of 42 different customer stories were collected, and through analysis, real-world examples of various customer relationship management goals, problems, and AI applications used as solutions were identified. As a result of the study, a theoretical model was developed for the utilisation of AI applications in SaaS companies, including key customer relationship management goals, challenges, and corresponding AI functionalities. Customer relationship management in SaaS companies primarily aims at business growth, which can be achieved by focusing on smaller, more precisely defined operational and strategic objectives. The study indicates that multiple AI functionalities can enhance the realisation of these goals while simultaneously reducing the impact of challenges associated with them. The thesis contributes to academic literature and particularly to the corporate management of SaaS companies. It enhances academic understanding of potential AI applications within the SaaS sector, providing benchmarks for other technologically advanced fields as well. Additionally, the research offers practical insights into how AI can be utilised comprehensively as part of a broader IT strategy. Future research is suggested to explore the effects of different AI functionalities through quantitative methods or qualitatively examine their impact on employees’ or customers’ experiences.