Surface negative sentiment and churn signals tied to specific customers.
You identify at-risk customers before they churn by surfacing negative sentiment and churn-related themes tied to individual accounts. Chattermill replaces manual review of feedback with automated signal detection, so your team can prioritise retention touches on the accounts most likely to leave.
This guide shows you how to open the at-risk view, review the feedback behind each signal, and route high-priority accounts to your retention team for follow-up.
Replace manual feedback review with an evidence-based customer-health summary.
Give management an evidence-based view of complaint drivers without manual compilation.
Show which issues most drive customers to leave so fixes target what matters.
Open the at-risk view in Chattermill.
Review customers showing negative sentiment or churn signals.
Read the feedback behind each signal.
Prioritise the accounts worth a retention touch.
Route them to the team for follow-up.
You open the at-risk view and see **[Customer A]** flagged with a churn signal. You read the feedback behind the signal: - Survey response: **"[Negative feedback about product or service]"** - Support conversation: **"[Customer concern or complaint]"** You prioritise this account based on the signal and route it to your retention team with context: **"Churn risk: [brief summary of feedback]. Recommend follow-up."**
Identify customers showing negative sentiment or churn-related themes in their feedback, with the signal behind each, prioritised for a retention follow-up.
See whether an issue is improving after a fix or getting worse.
Analyse NPS, CSAT, and support conversations together rather than piecemeal.