Monitoring customer health and churn risk manually is time-consuming and error-prone. Support teams spend hours each week pulling data from multiple systems, calculating scores inconsistently, and identifying at-risk customers too late to intervene effectively.
Automation consolidates all customer signals into a single health score, flags at-risk accounts in real time, and recommends tailored outreach strategies. the team receives alerts the same day risk emerges, enabling faster and more consistent intervention.
The full workflow, from trigger to completion.
Automation runs on a daily schedule or when a new support ticket, payment event, or usage change is detected. The trigger pulls the latest customer data from all connected systems.
Automation queries product analytics API and retrieves monthly active users, feature adoption, and login frequency for all accounts. Data is normalized and stored in memory.
Automation queries API and retrieves recent transactions, failed charges, subscription status, and MRR for each account. Payment health indicators are calculated.
Automation queries API and retrieves recent support tickets, NPS scores, and feedback sentiment. Ticket volume and resolution time are calculated.
The automation applies a consistent scoring model to all gathered data, weighing usage trends, payment health, support sentiment, and NPS. A health score (0-100) and churn risk level (low, medium, high) are assigned to each account.
Automation writes the calculated health score, risk level, and last-updated timestamp to each customer record. Custom fields are populated for reporting and filtering.
Automation sends a formatted message to the support channel listing all high-risk and medium-risk accounts, sorted by urgency. The message includes the health score, key risk factors, and a link to the customer record.
Automation creates a task or notification for the assigned account manager, prompting outreach to the at-risk customer. Task includes recommended talking points based on the risk factors.
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