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Renewals at risk and why

Composite signal: sentiment, support load, commitment drift, time-to-renewal.

Best for
Quarterly forecasting
Primitives
RenewalsSentimentCommitmentsDrift
Token savings
75%

The prompt

You are forecasting renewal risk for the next two quarters. Using Sentra: 1. List every Account with a renewal date in the next 180 days. 2. For each, compute four signals: - Sentiment trend over the last 90 days (improving / flat / declining). - Open Commitments past due, weighted by importance. - Support volume trend (Zendesk / Intercom / #cs Slack). - Mention drift — has the account quietly fallen out of internal conversation? 3. Combine into a single risk band (low / medium / high), with the two strongest signals named. Output a sorted list, highest risk first: | Account | Renewal date | Risk | Top two signals | Owner | For the top five, add a paragraph: what to do, who to loop in, and which Commitment, if closed in the next two weeks, would most move the needle.

See it work

Run it from

ClaudeCursorChatGPTAny agent · MCP

How it runs

  1. 01Copy the prompt into any agent connected to Sentra over MCP.
  2. 02The agent reads the company memory, scoped to what you are allowed to see.
  3. 03One grounded answer comes back, with every line traceable to its source.

Or automate it with Actions

The same prompt runs as a Sentra Action: on a schedule, or on a semantic trigger. Output lands in Slack or email, and every send waits for your approval.

Trigger idea: quarterly forecasting

Sentraover MCPComposite: sentiment, support load, commitment drift

Q4 renewals, ranked

Meridian · $220k · HIGH

composite · 9 signals

Outage aftermath plus three weeks of clipped replies plus an owed credit memo. The renewal call is not yet scheduled.

Harbor · $95k · MEDIUM

composite · 5 signals

Three weeks of silence and two undelivered commitments. No negative words, just absence.

Brightline · $60k · LOW

call · Aug 29

Healthy cadence, positive enablement session, expansion raised by them.

The prompt above becomes this. Fictional data, real mechanics: every line cites the meeting, thread, or record it came from.

Without Sentra

  1. 01Pull every account with a renewal date in the next 180 days from the CRM.
  2. 02For each, re-process the last 90 days of interactions to compute sentiment from raw text.
  3. 03Pull support volume from Zendesk/Intercom. Compute trend manually.
  4. 04Search internal Slack for account mentions. Estimate mention drift by counting per-week.
  5. 05Search emails and calls for unmet promises. Manually flag stale ones.
  6. 06Combine into a risk band per account. Compose the sorted list.
  7. 07For the top five, re-read material to suggest 'what to do this week'.

~200,000 tokens

With Sentra

  1. 01Filter `Accounts` where `renewal_date` is within 180 days.
  2. 02For each, pull `sentiment_trend`, `Commitments` with overdue status, support volume trend, and mention intensity — all typed signals.
  3. 03Combine into a composite risk band, with the two strongest signals named.
  4. 04Sort by risk. For the top five, pull the open `Commitment` that would most move the needle.
  5. 05Compose the report.

~50,000 tokens

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