In subscription-based software models such as SaaS, retaining customers over time requires continuous attention to how they engage with the product. Unlike traditional one-time purchases, SaaS customers can cancel their subscriptions at any time, which puts pressure on companies to demonstrate value on an ongoing basis. To meet this challenge, many organizations have adopted Customer Success Management (CSM) - a discipline focused on helping customers achieve outcomes through proactive engagement.

In practice, CSM teams are responsible for monitoring customer engagement, identifying signs of risk, and planning outreach strategies to support long-term satisfaction and retention. However, these decisions are often made with limited support from analytics tools. Many teams rely on fragmented data or intuition, making it difficult to consistently prioritize customers, evaluate feature adoption, or detect early signs of disengagement.

This study presents an analytics tool designed to support operational decision-making in Customer Success. The tool introduces four usage-based analytics that highlight customer engagement trends over time, across features, and at scale: a Usage Graph, Feature-Level Usage, Score-Based Analytic, and a Dashboard overview.

The results show that the tool helped the participating CS manager reassess several customers, uncover signs of disengagement that had previously gone unnoticed, and revise their outreach strategy accordingly. The tool also provided a clearer overview of customer engagement and improved confidence in prioritizing customers.