Understanding user navigation patterns from clickstream data is crucial for improving business software, yet remains challenging due to the complexity and variability of real-world environments. Unlike controlled settings, real-world clickstreams are noisy, fragmented, and often incomplete, due to session timeouts, network issues, caching, or third-party interactions—making it difficult to reconstruct coherent user journeys. Additionally, the absence of labeled data hinders the use of supervised learning, pushing researchers toward unsupervised or heuristic-based approaches that struggle to fully capture user behavior. In this paper, we present a benchmark of embedding techniques for modeling user navigation behavior on task-oriented software. We identify distinct user behaviors across three real-world case studies. Results show that Pattern2Vec outperforms Word2Vec in capturing meaningful task-based navigation patterns, confirming its suitability for clickstream analysis.