Detect At-Risk Accounts Before They Cancel, Not After

What happens: A customer's daily active users dropped from 45 to 8 over the past 3 weeks. Nobody on your team noticed because usage data lives in an analytics tool that CSMs don't check daily. Why it matters: Usage decline is the strongest predictor of churn. A 50%+ drop in engagement over 2 weeks signals disengagement. By week 4, the customer has already evaluated alternatives.

What happens: A customer submitted 6 support tickets last month. Three had frustrated language. The tickets got resolved individually, but nobody aggregated the sentiment pattern to flag the account as at-risk. Why it matters: Individual ticket resolution misses the bigger picture. A pattern of negative interactions across multiple tickets is a churn signal that no single ticket review catches.

What happens: The renewal is in 30 days. The CSM reaches out for the first time in 3 months. The customer has already decided not to renew because unresolved issues piled up without anyone checking in. Why it matters: Retention work that starts at renewal is rescue work, not prevention. The window for saving an account closes 60 to 90 days before renewal, not 30.

What happens: Each CSM has their own gut feel about which accounts are healthy and which are at risk. There is no consistent scoring model that combines usage, support, engagement, and payment data into a single health indicator. Why it matters: Without a standardized score, at-risk accounts hide in plain sight. The CSM with 50 accounts can't manually track health signals for each one every week.

What happens: A CSM identifies an at-risk account. There is no defined playbook for what to do next. Do they send an email? Schedule a call? Offer a discount? Escalate to leadership? Each CSM handles it differently with different outcomes. Why it matters: Without a standardized retention response, save rates depend on individual CSM skill and judgment rather than a tested, repeatable process.

What happens: A customer is using 90% of their seat allocation and hitting feature limits weekly. Nobody notices because the same team focused on preventing churn has no bandwidth to identify accounts ready for upsell. Why it matters: Churn prevention and expansion are two sides of the same coin. Healthy, growing accounts represent revenue expansion opportunities that go unnoticed when all attention goes to firefighting.

OpenClaw calculates a health score (0-100) for every account by combining usage data, login frequency, feature adoption, support ticket sentiment, NPS responses, and payment history. Scores update daily. Accounts scoring below your configured threshold (e.g., below 40) are flagged as at-risk automatically.

OpenClaw monitors daily and weekly active users, feature usage patterns, and session duration for every account. When usage drops more than 30% over a 2-week window, OpenClaw alerts the assigned CSM with the specific metrics that declined, which users stopped logging in, and how current usage compares to the account's historical baseline.

OpenClaw analyzes tone and language across all support tickets per account. A single frustrated ticket is normal. Three frustrated tickets in 2 weeks is a pattern. OpenClaw aggregates sentiment data and flags accounts where negative interactions are escalating. The CSM gets an alert with ticket history and sentiment trend, not just the latest ticket.

When an account drops below the health threshold, OpenClaw triggers a predefined retention sequence. Step 1: CSM alert with full context. Step 2: Personalized check-in email from the CSM. Step 3: Executive escalation if no response within 48 hours. Step 4: Special offer or account review call. Each step triggers automatically based on the account's response.

OpenClaw flags accounts approaching renewal (90, 60, 30 days out) with their current health score, usage trend, and open support issues. CSMs get a prioritized renewal list sorted by risk level. High-risk renewals get flagged for executive involvement. Healthy renewals get automated expansion opportunity signals.

OpenClaw identifies accounts showing growth signals: seat utilization above 80%, frequent hits on feature limits, requests for capabilities in higher tiers. These accounts get flagged for upsell conversations with specific data on what the customer is outgrowing. Retention and expansion tracked from the same health data.