An account can show signs of disengagement long before a renewal conversation begins. Users may stop returning to a core workflow, stall during onboarding, or skip a newly released feature. Product teams often see these signals only at the user level, while annual recurring revenue (ARR), plan, renewal date, and ownership data remain in a customer relationship management (CRM) system or data warehouse. That separation makes it difficult to tell whether a few inactive users represent normal variation or a high-value account that needs attention.
Datadog Product Analytics now supports account segments that bring business context and product behavior into the same analysis. You can define a reusable group from account attributes and events performed by users in those accounts, then apply it in funnels, retention, and Datadog Pathways. This workflow helps product and customer success teams investigate possible churn signals while keeping the account (rather than an individual user) as the unit of analysis.
In this post, we’ll explain how to:
Identify churn risk at the account levelEnrich account profiles with business contextBuild a segment from attributes and behaviorAnalyze an at-risk segment across Product AnalyticsShare account-level findings with customer success teams
