KI-gestützte Kundensuche im B2B-Vertrieb: Automatisierung der Lead-Generierung und Optimierung der Kundenakquise

Abstract

Increasing digitalization and data-driven orientation in B2B sales present companies with new challenges and opportunities in customer acquisition. Against this backdrop, this study examines the influence of artificial intelligence (AI) on lead generation and customer search. The aim is to find out which technologies are particularly suitable, how their use affects efficiency, quality and personalization and what specific challenges arise during implementation - especially with regard to data quality, integration and data protection. The study is based on a systematic literature review, in which both scientific articles and current practical studies were evaluated. Key technologies such as machine learning, natural language processing and predictive analytics were analyzed and compared with traditional methods of customer acquisition. In addition to the technical evaluation, the focus was also on the consideration of psychological and ethical factors. The results show that AI applications can significantly increase the efficiency of customer searches, for example through automated lead scoring, real-time analyses and a personalized approach. At the same time, the quality of leads improves as objective data patterns replace subjective assessments. Particularly in complex B2B markets, AI opens up the possibility of identifying relevant target groups more precisely and reducing wastage. However, implementation requires high data quality, sustainable system architectures and clear data protection concepts. Acceptance by employees and customers is also a challenge that should not be underestimated. In summary, the study shows that AI does not replace customer search in B2B sales, but rather effectively enhances it. Companies that invest in suitable technologies and data infrastructures can not only increase their ROI but also secure their long-term competitiveness. Future developments should therefore focus on practical implementation strategies and the promotion of data-driven corporate cultures.

Date of Award	2025
Original language	German (Austria)
Supervisor	David Muhr (Supervisor)