spot which accounts are at renewal risk before the renewal call

Customer success managers often realize an account is going to churn only after the renewal notice is ignored or the cancellation request arrives, because usage drops and support tickets pile up across different systems before anyone connects the dots.

Before
45 min
After
20 min
Saved
25 min
Step diagram: spot which accounts are at renewal risk before the renewal call — 6 steps, 3 handled by AI and 3 by you.

How this used to go

  • Open the CRM and filter for accounts with renewals coming up in the next 60 days.
  • Check the support ticketing system to see if recent complaints or unresolved issues exist for each account.
  • Review product usage logs or analytics dashboards to check if login frequency or key feature adoption has declined.
  • Scan recent email threads and meeting notes to gauge the customer's sentiment and identify any mentioned frustrations.
  • Compile the findings into a spreadsheet or notes document to decide which accounts need immediate intervention.

Checking four or five different systems for every single account means signs of trouble get missed when workloads are heavy.

The workflow, step by step

  1. You

    1. Assemble the renewal review data

    Export or copy the accounts renewing within 60 days, along with recent support tickets, usage indicators, email threads, and meeting notes. Include account names or IDs so the records can be matched across sources.

  2. AI

    2. Match records and organize account signals

    Give the collected records to AI and ask it to group them by account, separate recent from older information, and flag missing or conflicting data. Do not ask it to treat incomplete records as evidence of churn risk.

  3. AI

    3. Identify renewal-risk patterns

    Ask AI to flag combinations such as declining usage, unresolved or repeated support issues, negative language, and reduced engagement with customer contacts. Require each flag to cite the source record and label sentiment or intent as uncertain when the evidence is ambiguous.

  4. You

    4. Choose accounts and interventions

    Validate the highest-risk flags against the source records, then decide which accounts need immediate outreach, an internal support escalation, or routine monitoring. This decision determines where the team spends its limited intervention time, and AI should not make it alone.

  5. AI

    5. Prepare account action briefs

    Ask AI to turn the selected accounts into concise briefs containing the renewal date, evidence behind the risk flags, unresolved questions, the chosen intervention, and suggested talking points. Exclude unsupported claims about customer intent.

  6. You

    6. Approve actions and update the working records

    Use the briefs to contact customers or coordinate internal fixes, then record the owner, next action, and follow-up date in the CRM or team workspace. Correct any inaccurate AI summary before it is shared with the customer or used in a renewal decision.

What you end up with

A prioritized renewal-risk list with source-backed evidence, uncertainty notes, selected interventions, owners, follow-up dates, and approved account talking points.

Where this falls apart

  • Customer communications contain sarcastic or dry phrasing that the model misinterprets as genuine satisfaction, causing actual churn risks to be hidden from the prioritized list.
  • Support tickets lack clear resolution statuses or proper account linking, causing the model to treat old resolved complaints as active issues and falsely inflate the account risk score.

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