From the Sturdy team
Customer health intelligence, churn signals, and CS operations. What we learn from the data, and what it means for your renewal pipeline.
Most accounts that churn gave you signals weeks in advance. The problem is those signals live in support tickets and email threads your team is too busy to re-read at renewal time.
Product usage metrics tell you what customers are doing. They do not tell you how they feel about it. Those are different problems with different early-warning signals.
Surveys ask customers to describe their frustration after they have already decided to leave. Tickets record it as it happens. That gap was the whole reason we built Sturdy.
Support tickets capture written frustration. Call notes capture spoken frustration. Neither tool knows what the other recorded. Here is how connecting them creates a more complete signal.
Escalation volume is easy to track. The drop-off that comes after an escalation is not, and it is often a stronger churn predictor than the escalation itself.
Gong and Chorus record and transcribe. But most CS teams never go back and systematically analyze what topics cluster around accounts that churned versus those that renewed.
In our analysis of support and email patterns across early-access pilots, the signal that predicts a hard renewal conversation typically appears four to seven weeks before the conversation happens.
The leading CS platforms are built around product usage, NPS, and manual health scores. None of them read your support queue. This is an overview of the gap.
The accounts that surprise CSMs at renewal are rarely truly quiet. They had patterns in support and email that nobody had time to synthesize into a coherent view.
Email tone shift is one of the most reliable early signals in customer health analysis. Here is how NLP approaches it and where standard sentiment analysis falls short in a B2B context.
Quarterly business reviews are built around the last 90 days. But the decisions that will define the next 90 days are being made right now, in the tickets your customers are opening today.
Customers who are about to churn write tickets differently than customers who are satisfied. The language patterns are consistent enough to detect if you know what to look for.