Flag escalation and churn risk in open tickets
The system watches open conversations for repeated contact, sharp changes in tone, mentions of canceling or a competitor, and blown SLAs, then alerts the account owner with the evidence. For support and success teams that learn about unhappy customers too late.
Function: Support · Industry: Any industry
Flag escalation and churn risk in open tickets
6 steps
At a glance
- Trigger
- A ticket is updated, or a daily scan of open tickets runs.
- Typical inputs
- Ticket threads and sentiment history
- Customer plan, renewal date and value
- SLA clocks
- Escalation policy
- Systems it usually connects to
- Zendesk
- Salesforce
- Slack
- Gainsight
- How the work splits
4 rules, 1 model, 1 person steps
How it runs, step by step
6 steps, each tagged by what does the work. Rules are fixed logic, a model reads or drafts, and a person decides.
- Rules Fixed logic in code
- Model AI reads or drafts, with a confidence score
- Person A named person decides
- 1
Scan for hard signals
RulesRules detect repeat contacts on one issue, SLA breaches and keywords such as cancel, refund or legal.
- 2
Read for tone and intent
ModelA model assesses whether the latest messages show frustration, a threat to leave or a request for a manager.
- 3
Add account context
RulesRenewal date, plan value and recent product incidents are attached to the alert.
- 4
Alert the owner
RulesThe account owner and the support lead get a Slack message with a link, the evidence and the SLA state.
- 5
Decide the response
PersonThe owner chooses whether to call, offer a fix, escalate to engineering or leave it with the agent.
- 6
Track the outcome
RulesThe alert is closed with the action taken, so flags can be compared with what actually happened.
Where people approve
The decisions that stay with your team, whatever the confidence score says.
- Account owners decide on goodwill gestures and discounts.
- Support leads approve engineering escalations.
- Alert rules are reviewed monthly by support management.
KPIs to track
What to measure, before and after. Targets come from your own baseline.
- 01Alerts that led to an action
- 02Time from signal to owner contact
- 03Flagged accounts that renewed, against your baseline
- 04False alerts per week
Questions about this workflow
Does the model predict churn?
It flags signals in conversations. Predicting churn needs outcome data over time, and we do not claim it. The alerts are prompts for a person to look, with the evidence attached.
Will agents feel monitored?
The alerts concern customer conversations, not agent performance. We keep them separate from agent scorecards, and we recommend telling the team that up front.
Which steps use rules, a model and a person?
Rules: scan for hard signals; add account context; alert the owner; track the outcome. Model: read for tone and intent. Person: decide the response. Rules are fixed logic in code. A model reads or drafts and reports its confidence. A named person decides.
What do we need to start?
Typically: ticket threads and sentiment history; customer plan, renewal date and value; SLA clocks; escalation policy. You also need read access to Zendesk, Salesforce, Slack and Gainsight, or the equivalents you use. A workflow audit measures your volumes and past cases first, then gives a fixed price for the build, so you can decide with real numbers.