Most customer success teams react to churn instead of preventing it.
When your data lives in Gainsight (CS activities), Salesforce (account context), Stripe (billing), and BigQuery (warehouse), you can build a customer health score that predicts risk before it’s too late.
The 5 signals that predict churn
1. Product Usage
Monitor whether customers are using key features that drive value, track if usage is trending up or down over time, and identify customers stuck in onboarding who haven’t reached their “aha moment” yet.
2. Support Health
Watch for increasing support ticket volume which often indicates product problems, monitor whether tickets are resolving quickly or lingering, and identify recurring issues that suggest deeper problems.
3. Billing Health
Track payment failures that indicate financial stress or dissatisfaction, monitor whether customers are on payment plans which can signal cash flow issues, and check if they’re behind on invoices which often precedes churn.
4. Engagement Health
Monitor email open rates to see if customers are staying engaged, track webinar attendance to gauge interest in learning more, and watch response rates to CS outreach to identify disengaged accounts.
5. Account Health
Check if their champion is still at the company, monitor whether they’re expanding or contracting their usage, and track renewal dates to prioritize outreach before contracts expire.
What to build first (week 1)
Start with a simple customer health score that combines product usage from your product database tracking key features, support tickets from Gainsight or Zendesk showing volume and resolution times, billing health from Stripe including payment failures and dunning status, engagement metrics from Gainsight like email opens and meeting attendance, and account context from Salesforce including renewal dates and expansion opportunities.
Then calculate a health score as a weighted combination of these signals, assign a risk level of high, medium, or low, and generate recommended actions like CSM outreach, product fixes, or billing follow-up based on the specific signals triggering the risk.
Why most customer health scores fail
Most customer health scores fail because data is stale, updated monthly instead of daily, making it too slow to catch problems early. Signals are incomplete, missing critical indicators like product usage and billing health that predict churn. Context is missing when scores don’t compare accounts to similar customers, making it hard to understand if a score is actually concerning. Actionability is low when scores show risk but don’t indicate what to do about it.
When you connect all the signals, you can see risk before it’s too late to intervene. You can prioritize accounts that need attention based on actual risk signals rather than gut feeling. You can take action before churn happens by addressing issues proactively. Most importantly, you can measure the impact of CS activities to understand what actually works.
The hidden cost of reactive CS
When CS is reactive, you find out about churn after it happens, leaving you scrambling to save accounts that have already decided to leave. You can’t prevent churn proactively because you don’t see warning signs until it’s too late. You can’t measure CS impact because you don’t know which activities actually prevent churn. Most critically, you can’t optimize CS activities because you’re always reacting instead of learning what works.
Proactive CS means you see risk before it’s too late, giving you time to intervene and save accounts. You can prevent churn proactively by addressing issues before customers decide to leave. You can measure CS impact by tracking which interventions actually work. Most importantly, you can optimize CS activities based on data rather than assumptions.