Cm renewal forecaster

Renewal Forecaster

Produces probability-weighted renewal forecasts by combining health signals, usage data, engagement patterns, and historical outcomes. Part of the Commercial Motion pillar.

This is an analytical and reporting skill. It produces forecasts and projections. It does not make retention decisions or set strategy -- those are human calls informed by the forecast.

When to Run

• Scheduled: Weekly forecast refresh for all accounts renewing in next 180 days
• On-demand: When leadership requests a forecast update
• Triggered: When a significant risk or health signal changes on an account within the renewal window

Core Execution Logic

Step 1: Identify Renewal Cohort

Pull all accounts with renewal dates in the next 180 days. For each, assemble:

• Current health score and trend (from bi-health-score)
• Risk signal count and severity (from bi-risk-detector)
• Usage trend classification (from bi-usage-monitor)
• Engagement pattern (from ra-engagement-tracker)
• Renewal risk classification (from lo-renewal-manager: On Track / Watch / At Risk / Critical)
• Expansion signals (from bi-expansion-detector -- renewals with expansion opportunity are a different conversation)
• Contract details (ARR, term, auto-renewal clause, notice period)

Step 2: Assign Renewal Probability

Default probability model (use until historical calibration data is available):

Risk Classification	Base Probability	Positive Adjustments	Negative Adjustments
On Track	95%	+2% if health >85 and stable for 90+ days	-5% if any single risk signal present; -10% if engagement declining
Watch	75%	+10% if active save play showing health improvement	-10% per additional risk signal; -5% if usage declining; -15% if competitive signal
At Risk	45%	+15% if executive engaged and customer responding to intervention	-10% if competitive signal; -15% if champion departed; -10% if timeline <60 days with no progress
Critical	15%	+20% if customer explicitly willing to negotiate (not silent)	-10% if timeline <30 days; -15% if customer stated intent to leave

Probability bounds: Floor at 5% (even "certain" churns occasionally reverse). Cap at 98% (even "certain" renewals occasionally surprise).

Calibration: After 12 months of data, replace the default model with historically-calibrated probabilities. See references/probability-calibration.md.

Step 3: Compute Portfolio Forecast

Aggregate individual account probabilities into portfolio-level projections:

Metric	Computation
Expected retained ARR	Sum of (account ARR * renewal probability) for all renewing accounts
Expected churn ARR	Sum of (account ARR * (1 - renewal probability))
Expected expansion ARR	Sum of (expansion opportunity value * expansion probability) for accounts with both renewal and expansion
Forecast NRR	(Expected retained ARR + Expected expansion ARR) / Total renewing ARR
Optimistic scenario (80% CI)	Assume all Watch accounts renew, At Risk accounts at 60%, Critical at 25%
Pessimistic scenario (80% CI)	Assume Watch accounts at 60%, At Risk at 30%, Critical at 5%
Most likely scenario	Use the computed probabilities as-is

Step 4: Identify Actionable Accounts

Flag accounts where intervention could meaningfully change the forecast:

Category	Criteria	Why Actionable
Saveable At Risk	At Risk, renewal >60 days away, no competitive signal, root cause is addressable	Enough time and a path to resolution exists
Critical but engaged	Critical, customer is still communicating and willing to discuss	Window is closing but customer has not fully disengaged
Watch trending down	Watch, health declining for 2+ consecutive periods	Currently probable renewal, but trajectory suggests it may become At Risk without intervention
Expansion at renewal	On Track with active expansion signal and renewal in next 90 days	Opportunity to convert a renewal into a growth event

Step 5: Generate Forecast Report

{
  "forecast_date": "2026-03-10",
  "horizon": "next_180_days",
  "portfolio": {
    "renewing_accounts": 42,
    "renewing_arr": 3200000
  },
  "forecast": {
    "most_likely": {
      "retained_arr": 2880000,
      "churn_arr": 320000,
      "expansion_arr": 180000,
      "nrr": 0.956
    },
    "optimistic": { "nrr": 0.982 },
    "pessimistic": { "nrr": 0.921 }
  },
  "by_classification": {
    "on_track": { "count": 28, "arr": 2100000, "avg_probability": 0.94 },
    "watch": { "count": 8, "arr": 680000, "avg_probability": 0.72 },
    "at_risk": { "count": 4, "arr": 340000, "avg_probability": 0.41 },
    "critical": { "count": 2, "arr": 80000, "avg_probability": 0.13 }
  },
  "actionable_accounts": [
    {
      "account": "Acme Corp",
      "arr": 85000,
      "renewal_date": "2026-05-15",
      "days_remaining": 66,
      "classification": "at_risk",
      "probability": 0.45,
      "category": "saveable_at_risk",
      "top_risk": "Champion departed February, usage declining since",
      "intervention_opportunity": "New VP Engineering hired last week -- window to establish relationship before renewal window",
      "csm": "Jane Doe"
    }
  ],
  "period_over_period": {
    "nrr_change_vs_last_week": -0.003,
    "accounts_worsened": 2,
    "accounts_improved": 1,
    "new_at_risk": 1
  }
}

Handoff to Human

Decision Point	Context Provided	What the Human Decides
Forecast review	Full forecast with scenarios and actionable accounts	Whether the probabilities match their qualitative intelligence. CSM managers override individual account probabilities based on relationship context
Resource allocation	Actionable accounts ranked by save potential and ARR	Which accounts to invest save effort in, given limited CSM capacity
Executive communication	Forecast with confidence intervals	How to present the forecast to the CRO/CFO -- which scenario to emphasise, what narrative to frame around the numbers
Expansion timing	Accounts with both renewal and expansion signals	Whether to pursue expansion at renewal (efficient) or separate the conversations (less risky)

Confidence and Limitations

• High confidence for timeline tracking and milestone computation -- deterministic calendar logic
• Medium confidence for probability estimation using the default model -- health and signal data are strong predictive inputs, but the model has not been calibrated against your specific portfolio's churn patterns until you have 12 months of data
• Medium confidence for portfolio aggregation -- individual account probabilities may be off, but errors tend to cancel out at the portfolio level (some pessimistic, some optimistic). The aggregate forecast is more reliable than any individual account prediction
• Low confidence for accounts with limited data history (<6 months) or accounts where the risk is purely relationship-based with no data signal
• Probability estimates should be treated as directional, not precise. "75% renewal probability" means "more likely than not with moderate risk," not a guarantee
• The forecast does not account for portfolio-level events (pricing changes, product launches, market shifts) that may move multiple renewals simultaneously. The human must factor these in
• The optimistic/pessimistic scenarios are bounded estimates, not worst-case/best-case. True worst case (major product failure, market crash) is outside the model

Dependencies

Required:

• lo-renewal-manager (renewal classifications, timeline data)
• bi-health-score (health data for probability computation)
• CRM API (contract data, ARR, renewal dates)

Strongly recommended:

• bi-risk-detector (risk signal count and severity)
• bi-usage-monitor (usage trend for trajectory assessment)
• bi-expansion-detector (expansion signals for combined renewal-expansion opportunities)
• ra-engagement-tracker (engagement pattern for probability adjustment)

Downstream consumers:

• CS leadership (weekly forecast review)
• CRO/CFO (monthly/quarterly revenue planning)
• Board reporting (quarterly NRR projection)
• ic-cross-func-prep (pipeline review materials)

References

• references/probability-calibration.md -- Historical calibration methodology, minimum data requirements, and model validation process