Renewal Predictor

Renewal Predictor

Predict client renewal likelihood by computing a multi-dimensional Health Score and generating an actionable, evidence-backed risk assessment for every account.

Core Mission

For every account, answer three questions:

1. Will this client renew, and with what confidence? 2. What signals are driving that prediction? 3. What specific intervention should the team execute right now?

Contents

• references/dimension-rubrics.md -- 0-100 scoring criteria, data sources, red/green flags for all 7 dimensions
• references/confidence-and-scoring.md -- composite formula, thresholds, confidence calibration, missing-data rules, edge cases
• references/signals.md -- compound churn and expansion signals with severity tiers
• references/save-plays.md -- intervention templates by priority
• references/output-template.md -- exact structure for renewal-forecast.md
• references/data-templates.md -- CSV templates to request when data is missing

Health Score Model

The Health Score is a composite metric from 0 to 100, built from seven weighted dimensions. Score each dimension independently on a 0-100 scale using references/dimension-rubrics.md, then combine with these weights:

Map the composite score to a prediction category (80-100 Likely to Renew, 60-79 Neutral/Monitor, 40-59 At Risk, 0-39 Likely to Churn). See references/confidence-and-scoring.md for the formula, thresholds, and confidence rules.

Execution Protocol

1. Discover available data. Use Glob to find CSV, JSON, YAML, or XLSX files, CRM exports, meeting notes, or communication logs in the working directory. Use Grep for account names, metric patterns, and keywords like "churn," "cancel," "renew," "escalat," "competitor," "discount," "downgrade." If no data is found, request the templates in references/data-templates.md and stop. 2. Parse and normalize. For each account, extract values for as many of the seven dimensions as the data supports. Normalize every metric to the 0-100 scale in references/dimension-rubrics.md. Flag insufficient, missing, or ambiguous dimensions. Record the raw evidence behind each score. 3. Compute Health Scores. Score each dimension, apply the weights, compute the composite, map to a prediction category, and determine confidence per references/confidence-and-scoring.md (including the missing-data rules). 4. Detect signals. Scan for churn and expansion signals in references/signals.md. Cross-reference across dimensions for compound signals. Tag each with severity and triggering evidence. 5. Generate interventions. For At Risk or Likely to Churn accounts, identify the root cause, select the most impactful play from references/save-plays.md, and assign priority by ARR at risk, renewal proximity, and signal severity, with a clear owner, deadline, and success metric. For Likely to Renew accounts with expansion signals, suggest a specific upsell or cross-sell motion and its trigger. 6. Write the forecast. Generate renewal-forecast.md following references/output-template.md. Support every claim with evidence; make every recommendation actionable and specific.

Behavioral Guidelines

1. Evidence over intuition. Every score must cite the specific data that produced it. If data is missing, say so explicitly. Never infer a score without evidence. 2. Conservative predictions. When signals conflict, weight negative signals more heavily. A false At Risk flag is cheaper than a missed churn signal. 3. Actionable specificity. "Improve engagement" is not a recommendation. "Schedule a QBR with [Champion Name] by [Date] to review Q3 usage trends and introduce the new reporting module" is. 4. No false precision. Report whole-number scores. Do not claim 92% confidence when data covers three of seven dimensions. 5. Temporal awareness. Weight recent signals more heavily than older ones, and note the recency of the data behind each score. 6. Portfolio thinking. Identify systemic themes (e.g., "4 of 12 accounts have champion turnover this quarter") and surface them in the executive summary. 7. Renewal proximity urgency. Accounts renewing within 90 days get elevated priority regardless of health score. 8. Think in ARR. Frame every classification and priority in terms of revenue impact. 9. Assume nothing about data maturity. Adapt depth and precision to the available data, and state what additional data would improve the forecast. 10. Never refuse to produce a forecast because data is incomplete. Work with what is available, flag what is not, and deliver with clear confidence markers.

Confidence Calibration and Missing-Data Scoring

Composite Score Calculation

Health Score = (Engagement * 0.20) + (Support * 0.15) + (Adoption * 0.20) +
              (NPS_CSAT * 0.10) + (Billing * 0.10) + (Stakeholder * 0.10) +
              (Usage * 0.15)

Round to the nearest whole number.

Scoring Thresholds

Confidence Calibration

Every prediction must include a confidence level. Confidence is determined by two factors: the quality of available data and the consistency of signals across dimensions.

High Confidence (80-100%):

• Data is available for 6+ of the 7 dimensions
• Signals are consistent (all pointing in the same direction)
• Trailing data covers 90+ days
• At least one direct human signal (survey response, meeting notes, email sentiment)

Medium Confidence (50-79%):

• Data is available for 4-5 of the 7 dimensions
• Signals are mostly consistent with 1-2 conflicting indicators
• Trailing data covers 30-90 days
• Human signals are indirect or absent

Low Confidence (below 50%):

• Data is available for 3 or fewer dimensions
• Signals are mixed or contradictory
• Trailing data covers less than 30 days
• No direct human signals available

Adjustments

• If the account is less than 90 days old, cap confidence at Medium regardless of signal consistency. There is not enough history to predict with high confidence.
• If the account is in a known seasonal industry, adjust usage trend scores for seasonality before computing the health score.
• If a critical data source is missing (no NPS data, no support tickets logged, no usage analytics), note it as a data gap and reduce confidence by one level.

Scoring When Data Is Missing

When data for a dimension is unavailable:

1. Check if indirect signals exist (e.g., no NPS data but QBR notes mention satisfaction levels). 2. If indirect signals exist, score conservatively (cap at 60 for that dimension) and note the inference. 3. If no data exists at all, score the dimension at 50 (neutral) and flag it as a data gap. 4. Reduce overall confidence level by one tier for every 2 dimensions with missing data. 5. Never score a dimension at 0 unless there is affirmative evidence of a problem -- absence of data is not evidence of a problem.

Edge Cases

• New clients (less than 90 days): Score based on onboarding completion and early engagement signals. Use onboarding health as a proxy for feature adoption. Cap confidence at Medium regardless of signal consistency.
• Multi-product clients: Score each product relationship separately, then roll up to an account-level composite. Note which product line carries the most risk.
• Channel or partner-managed clients: Note that direct signal access may be limited. Weight partner feedback and escalations more heavily. Flag as a data quality limitation.
• Enterprise accounts with long sales cycles: Renewal conversations may start 6-12 months early. Adjust "days until renewal" urgency thresholds accordingly.
• Seasonal businesses: Adjust usage trend scoring for known seasonal patterns before computing health scores. A 30% dip in retail usage during Q1 may be normal, not alarming.
• Recently acquired companies: Treat as high-uncertainty accounts. Stakeholder continuity is likely disrupted. Cap confidence at Low until new organizational structure stabilizes.

Data Format Templates

If no data is available, provide these templates for the user to populate, then re-run the forecast.

Account Overview Template

account_name,arr,renewal_date,contract_start,contract_term_months,tier,seats_purchased,csm_name,executive_sponsor_client,champion_name,champion_title,champion_email

Usage Metrics Template

account_name,date,dau,wau,mau,sessions,avg_session_duration_min,api_calls,data_volume_gb,features_used_count,total_features_available

Support Ticket Template

account_name,ticket_id,created_date,resolved_date,severity,category,sentiment,csat_score,escalated,summary

NPS/CSAT Template

account_name,survey_date,survey_type,score,verbatim_comment,respondent_name,respondent_title

Billing Template

account_name,invoice_date,amount,due_date,paid_date,days_late,dispute_flag,discount_pct,notes

Stakeholder Template

account_name,contact_name,title,role_in_deal,last_activity_date,status,linkedin_url,notes

Engagement Log Template

account_name,date,activity_type,initiated_by,attendees,summary,next_steps

Dimension Scoring Rubrics

Score each dimension independently on a 0-100 scale, then combine with the weights in SKILL.md.

1. Engagement Frequency (20%)

Measures how actively the client participates in the relationship, both inside the product and with the team.

Scoring criteria:

• 90-100: Daily active usage by multiple users. Regular proactive outreach from client. Attends QBRs, participates in beta programs, provides roadmap feedback. Multiple threads of communication active simultaneously.
• 70-89: Weekly active usage. Responsive to outreach within 24 hours. Attends scheduled meetings. Engages with at least one non-support channel (community, events, feedback programs).
• 50-69: Usage several times per month. Responds to outreach within 48-72 hours. Attends most scheduled meetings but rarely initiates contact. Communication is primarily reactive.
• 30-49: Usage is sporadic or declining to once or twice per month. Slow to respond to outreach (3-5 business days). Cancels or reschedules meetings. No engagement outside of required touchpoints.
• 0-29: Usage has dropped to near-zero or is confined to a single user. Outreach goes unanswered for a week or more. Meetings are declined. No response to escalation attempts.

Data sources to check:

• Product analytics: DAU/WAU/MAU per account, session duration, login frequency
• CRM activity logs: emails, calls, meetings logged against the account
• Meeting attendance records: QBRs, check-ins, training sessions
• Community participation: forum posts, event registrations, beta enrollments
• Response latency: average time to reply to CSM outreach

Red flags:

• Login frequency dropped more than 40% over trailing 30 days
• No response to last two outreach attempts
• Primary contact has not logged in for 14+ days
• Declined last QBR or annual review

Green flags:

• Client proactively requests meetings or feature discussions
• Multiple departments or teams now using the product
• Client volunteered for case study, referral, or advisory board

2. Support Ticket Volume and Sentiment (15%)

Measures the health of the support relationship: trajectory, severity, and emotional tone, not just volume.

Scoring criteria:

• 90-100: Minimal ticket volume (below cohort average). Tickets are feature requests or minor questions, not bugs or outages. Sentiment in tickets is neutral to positive. CSAT on resolved tickets is consistently 4.5+/5.
• 70-89: Ticket volume is at or slightly below cohort average. Most tickets are low to medium severity. Sentiment is neutral. Resolution times meet SLA. No escalations in the trailing 90 days.
• 50-69: Ticket volume is above cohort average or trending upward. Mix of medium and high severity tickets. Some negative sentiment detected. One or two escalations in the trailing 90 days. Resolution satisfaction is inconsistent.
• 30-49: Ticket volume is significantly above average or has spiked sharply. Multiple high-severity or critical tickets. Negative sentiment is dominant. Escalations to management have occurred. Client has expressed frustration with support quality or speed.
• 0-29: Critical outage tickets or data loss incidents. Client has threatened to cancel or referenced competitors in support threads. Executive escalation has occurred. Sentiment is hostile or resigned. Open tickets are aging without resolution.

Data sources to check:

• Support platform: ticket count by severity, open vs closed, age of open tickets
• Sentiment analysis on ticket text and follow-up communications
• CSAT scores on resolved tickets (trailing 90 days)
• Escalation log: any tickets escalated beyond Tier 1
• SLA adherence: percentage of tickets resolved within SLA

Red flags:

• Ticket volume increased more than 50% month-over-month
• Any P0/P1 (critical/high) ticket open for more than 5 business days
• Client used words like "unacceptable," "considering alternatives," "escalate," "cancel"
• CSAT on support interactions dropped below 3.0/5

Green flags:

• Ticket volume is stable or declining while usage grows (indicates product maturity)
• Client submits feature requests rather than bug reports
• Support interactions end with positive feedback or thanks

3. Feature Adoption (20%)

Measures how much of the product the client is actually using relative to what they purchased and what is available.

Scoring criteria:

• 90-100: Client uses 80%+ of purchased features. Has adopted features released in the last two quarters. Uses advanced/power-user features. Multiple workflows configured. API integrations active. Self-service capabilities fully leveraged.
• 70-89: Client uses 60-79% of purchased features. Has tried at least one feature released in the last two quarters. Core workflows are well-established. Some advanced features in use.
• 50-69: Client uses 40-59% of purchased features. Adoption of new features is slow or absent. Usage is concentrated in one or two core workflows. Advanced features are untouched. Training opportunities have been offered but not taken.
• 30-49: Client uses less than 40% of purchased features. Multiple purchased modules are inactive. No adoption of new features in the last two quarters. Usage pattern suggests the client may not understand the full value of what they bought.
• 0-29: Client uses a single feature or module only. Most of the product is unexplored. No integrations configured. Usage pattern is indistinguishable from a free trial despite being on a paid plan.

Data sources to check:

• Feature usage matrix: which features are active per account
• Module activation rates: percentage of purchased modules in active use
• New feature adoption: time-to-first-use for features released in the last 6 months
• Integration status: API connections, webhooks, SSO, third-party integrations
• Training completion: onboarding milestones, certification progress

Red flags:

• Purchased module has zero usage for 60+ days after activation
• No adoption of any feature released in the last 6 months
• Client declined training or enablement sessions
• Usage is limited to a single user on a multi-seat license

Green flags:

• Client adopted a new feature within 30 days of release
• API usage indicates deep integration into client workflows
• Client requests features that indicate long-term investment in the platform
• Multiple roles/personas active in the product

4. NPS/CSAT Scores (10%)

Measures explicit satisfaction signals from surveys and structured feedback.

Scoring criteria:

• 90-100: NPS response is Promoter (9-10). CSAT consistently above 4.5/5. Client has provided testimonial or referral. Positive trend across last three survey cycles.
• 70-89: NPS response is Promoter (9-10) or high Passive (7-8). CSAT between 4.0-4.5/5. Scores are stable or improving.
• 50-69: NPS response is Passive (7-8). CSAT between 3.5-4.0/5. Scores are flat. Comments are neutral with no strong positive or negative signals.
• 30-49: NPS response is low Passive (7) or Detractor (0-6). CSAT between 2.5-3.5/5. Scores are declining. Comments reference specific pain points or unmet expectations.
• 0-29: NPS response is Detractor (0-6). CSAT below 2.5/5. Scores have dropped sharply. Comments reference intent to leave, competitor evaluation, or fundamental dissatisfaction.

Data sources to check:

• NPS survey results: score, verbatim comments, response rate
• CSAT surveys: transactional (post-support) and relational (quarterly/annual)
• In-app feedback: thumbs up/down, feature ratings, feedback widgets
• Third-party review sites: G2, Capterra, TrustRadius (if client has posted)

Red flags:

• NPS dropped 3+ points between survey cycles
• Client declined to respond to last survey (non-response is a signal)
• Verbatim comments mention competitor names or "looking for alternatives"
• CSAT trend is negative across three consecutive measurements

Green flags:

• Client is a Promoter and has provided a referral or review
• NPS improved by 2+ points between cycles
• Unsolicited positive feedback received outside of surveys

5. Billing History (10%)

Measures the financial health of the relationship and the client's commitment signals expressed through purchasing behavior.

Scoring criteria:

• 90-100: All invoices paid on time or early. No disputes in the last 12 months. Client has expanded contract (upsell, additional seats, upgraded tier). Multi-year agreement in place. No discount dependency.
• 70-89: Invoices paid within terms (net 30/45/60 as agreed). Rare minor disputes resolved quickly. Contract is stable with no downgrades. Renewal at same or higher value expected.
• 50-69: Occasional late payments (1-2 in the last 6 months). One invoice dispute in the last 12 months. Client requested pricing review or discount at last renewal. Contract value is flat.
• 30-49: Multiple late payments. Active invoice disputes. Client has downgraded seats, modules, or tier in the last 12 months. Heavy discount dependency (renewal contingent on discount). Budget review or procurement audit in progress.
• 0-29: Invoices are significantly overdue (60+ days). Client has requested early termination terms or referenced cancellation clauses. Chargeback or payment failure occurred. Finance team has flagged the account for collections risk.

Data sources to check:

• Billing platform: payment history, days sales outstanding (DSO) per account
• Invoice dispute log: frequency, severity, resolution
• Contract modifications: upsells, downgrades, tier changes, add-ons
• Discount history: percentage of contract value attributed to discounts
• Renewal terms: auto-renew status, opt-out window, multi-year vs annual

Red flags:

• Payment is 30+ days past due with no communication
• Client requested contract termination terms or asked about cancellation process
• Downgraded from a higher tier or reduced seat count
• Discount has increased at each renewal cycle

Green flags:

• Client expanded contract mid-cycle without being asked
• Multi-year renewal signed
• Client paying above list price due to custom SLA or premium support
• No discount requests at renewal

6. Stakeholder Continuity (10%)

Measures the stability of the human relationships that sustain the account, because products do not renew themselves -- people do.

Scoring criteria:

• 90-100: Executive sponsor is engaged and accessible. Primary champion is active, enthusiastic, and has organizational influence. Multiple stakeholders across departments are invested. No key personnel changes in the last 6 months. Succession plan exists if champion leaves.
• 70-89: Executive sponsor is aware of the relationship and available when needed. Champion is active and supportive. At least two stakeholders are familiar with the product. No disruptive personnel changes in the last 6 months.
• 50-69: Executive sponsor is disengaged or unknown. Champion is present but their influence may be limited. Only one or two people at the client know the product well. A key stakeholder changed roles in the last 6 months but a replacement was identified.
• 30-49: Champion has left the organization or changed roles with no clear successor. Executive sponsor is unknown or has changed. New stakeholders are unfamiliar with the product and have not been onboarded. Organizational restructuring is underway.
• 0-29: All known champions have departed. No executive sponsor. New decision-makers have no prior relationship with the team. Acquisition, merger, or leadership overhaul is in progress. New stakeholders are evaluating competitive alternatives.

Data sources to check:

• CRM contact records: role changes, departure flags, last activity dates
• LinkedIn monitoring: job changes for key contacts (champion, sponsor, admin)
• Meeting attendance: who is showing up vs who used to show up
• Org chart changes: restructuring announcements, new leadership, M&A news
• Onboarding status of new contacts: have replacements been introduced and enabled

Red flags:

• Champion changed jobs (LinkedIn update, email bounce, CRM flag)
• Executive sponsor was replaced and new sponsor has not been briefed
• New procurement or IT leadership with no prior relationship
• Client company announced layoffs, restructuring, or acquisition
• Decision-maker mentioned "evaluating options" or "conducting a review"

Green flags:

• Champion got promoted (more influence, deeper investment)
• New executive sponsor proactively reached out to learn about the partnership
• Client introduced additional stakeholders to expand the relationship
• Multi-threaded relationship: 5+ contacts actively engaged

7. Usage Trends (15%)

Measures the directional momentum of product usage. This is distinct from feature adoption -- it captures whether usage is accelerating, stable, or decelerating.

Scoring criteria:

• 90-100: Usage is growing across all key metrics (sessions, active users, data volume, API calls) over trailing 30/60/90-day windows. Growth rate is accelerating or steady. New use cases are emerging. Usage exceeds contracted entitlements.
• 70-89: Usage is stable or growing slightly. Key metrics are flat to positive over trailing 90 days. No significant declines in any dimension. Seasonal patterns are consistent with prior years.
• 50-69: Usage is flat overall with some metrics declining. Active user count may have dipped. Session duration or frequency is trending down slightly. No new use cases have emerged in the last quarter.
• 30-49: Usage is declining across multiple metrics. Active user count has dropped by 20%+ over trailing 90 days. Core workflows are being used less frequently. Data volume or API calls have decreased.
• 0-29: Usage has collapsed. Active users have dropped by 50%+ or to a single user. Core workflows are abandoned. The client appears to be migrating away or has stopped using the product entirely.

Data sources to check:

• Product analytics: trailing 30/60/90-day trends for sessions, users, actions, data volume
• API call logs: volume trends, new endpoints being called, error rates
• Storage/compute consumption: growing or shrinking footprint
• Feature-level usage trends: are core features stable while peripheral features decline, or vice versa
• Cohort comparison: how does this client's usage trajectory compare to similar accounts

Red flags:

• Active users declined 20%+ in the last 30 days without a known seasonal cause
• Core feature usage dropped while overall login count remained stable (indicates disengagement from value-driving workflows)
• Data exports increased (potential sign of migration)
• API call volume dropped or shifted to read-only patterns (extracting data, not writing)

Green flags:

• Usage is growing faster than seat count (organic adoption)
• New API endpoints being called (indicates expanding integration)
• Usage survived a slow season without significant decline
• Client hit or exceeded usage entitlements (expansion opportunity)

Output Template: renewal-forecast.md

Generate a file called renewal-forecast.md in the working directory following this exact structure. Every claim must be supported by evidence. Every recommendation must be actionable and specific.

# Renewal Forecast

Generated: [YYYY-MM-DD]
Accounts Analyzed: [N]
Data Sources: [list of sources consulted]

## Executive Summary

[3-5 sentences summarizing the overall portfolio health. Include: number of accounts in each category, total ARR at risk, top systemic themes, and the single most urgent action item.]

## Portfolio Overview

| Account | Health Score | Prediction | Confidence | ARR | Renewal Date | Top Risk Signal | Recommended Action |
|---|---|---|---|---|---|---|---|
| [Account 1] | [0-100] | [Likely to Renew / At Risk / Likely to Churn] | [High/Medium/Low] | [$X] | [YYYY-MM-DD] | [Signal] | [Action] |
| [Account 2] | ... | ... | ... | ... | ... | ... | ... |

## Detailed Assessments

### [Account Name] -- [Prediction Category]

**Health Score: [X]/100 | Confidence: [High/Medium/Low] | ARR: [$X] | Renewal: [YYYY-MM-DD]**

#### Dimension Scores

| Dimension | Score | Weight | Weighted Score | Trend |
|---|---|---|---|---|
| Engagement Frequency | [X] | 20% | [X] | [Up/Stable/Down] |
| Support Ticket Volume/Sentiment | [X] | 15% | [X] | [Up/Stable/Down] |
| Feature Adoption | [X] | 20% | [X] | [Up/Stable/Down] |
| NPS/CSAT | [X] | 10% | [X] | [Up/Stable/Down] |
| Billing History | [X] | 10% | [X] | [Up/Stable/Down] |
| Stakeholder Continuity | [X] | 10% | [X] | [Up/Stable/Down] |
| Usage Trends | [X] | 15% | [X] | [Up/Stable/Down] |
| **Total** | | **100%** | **[X]** | |

#### Active Signals

**Churn Signals:**
- [Signal 1]: [Description with specific data points]
- [Signal 2]: [Description with specific data points]

**Expansion Signals:**
- [Signal 1]: [Description with specific data points]

#### Evidence

[Specific data points, quotes from communications, metrics with dates. Every claim in the assessment must be traceable to evidence listed here.]

#### Recommended Action

**Priority**: [Critical / High / Medium / Low]
**Action**: [Specific intervention with clear next step]
**Owner**: [Role responsible -- CSM, CS Leader, Executive Sponsor, Support Engineering]
**Deadline**: [Date by which the action must be completed]
**Expected Outcome**: [What success looks like if the action is executed]

[Repeat the Detailed Assessment block for every account analyzed.]

## Priority Action List

Ranked list of interventions sorted by urgency and impact. This is the operational output that the CS team should execute against.

| Priority | Account | Action | Owner | Deadline | Risk if Delayed |
|---|---|---|---|---|---|
| 1 | [Account] | [Action] | [Owner] | [Date] | [What happens if this is not done] |
| 2 | [Account] | [Action] | [Owner] | [Date] | [Risk] |
| ... | ... | ... | ... | ... | ... |

## Early Warning Watchlist

Accounts that are currently healthy but showing early signals that could deteriorate. These are not yet at risk but should be monitored more closely over the next 30-60 days.

| Account | Health Score | Warning Signal | Monitoring Cadence | Trigger for Escalation |
|---|---|---|---|---|
| [Account] | [X] | [Signal] | [Weekly/Biweekly] | [What would cause this to move to At Risk] |

## Data Gaps

Accounts or dimensions where insufficient data prevented a confident assessment. These gaps should be filled to improve future forecast accuracy.

| Account | Missing Data | Impact on Assessment | Recommended Collection Method |
|---|---|---|---|
| [Account] | [What is missing] | [How it affected the score] | [How to get this data] |

## Methodology Notes

- Health Score model version: 1.0
- Weights last calibrated: [Date]
- Data coverage period: [Start date] to [End date]
- Known limitations: [Any caveats about data quality, missing sources, or model assumptions]

Save Plays Library

Reference these intervention templates when generating recommended actions. Match the play to the account's root cause and prediction category.

Critical Priority (Likely to Churn)

Executive Sponsor Alignment

• Trigger: Champion departed, executive sponsor disengaged, or competitive evaluation in progress
• Action: CS leadership or the executive sponsor requests a meeting with the client's decision-maker. Frame as a partnership review, not a save attempt. Come with a value summary, roadmap preview, and a specific offer (extended support, custom feature, pricing restructure).
• Timeline: Within 5 business days
• Success metric: Meeting scheduled and held; competitive evaluation paused or cancelled

Emergency Technical Review

• Trigger: Critical support issues unresolved, platform stability concerns, or data integrity questions
• Action: Deploy a solutions engineer or senior technical resource for a dedicated review of the client's environment. Produce a written remediation plan with committed timelines. Provide a temporary dedicated support channel (Slack, direct line).
• Timeline: Within 3 business days
• Success metric: All critical issues have a resolution plan with committed dates; client confirms confidence is restored

Value Realization Workshop

• Trigger: Low feature adoption, client does not see ROI, or "what are we paying for" sentiment detected
• Action: Conduct a half-day working session (virtual or on-site) to map the client's current workflows against product capabilities. Identify three quick wins they can implement immediately. Build a 90-day value plan with measurable outcomes.
• Timeline: Within 10 business days
• Success metric: Client activates at least 2 new features within 30 days; ROI narrative is documented

High Priority (At Risk)

Stakeholder Re-engagement Campaign

• Trigger: Single-threaded relationship, new stakeholders not onboarded, or QBR attendance declining
• Action: Identify 3-5 additional stakeholders who should be involved. Send personalized outreach with role-specific value propositions. Offer tailored training sessions. Introduce them to relevant customer community peers.
• Timeline: Within 10 business days
• Success metric: 2+ new stakeholders engaged within 30 days

Custom Success Plan

• Trigger: Health score between 40-59 with no clear single root cause
• Action: Build a 90-day mutual success plan co-authored with the client. Define 3-5 measurable goals tied to their business objectives. Schedule biweekly check-ins to track progress. Provide a dedicated resource for the plan duration.
• Timeline: Plan drafted within 7 business days, first check-in within 14
• Success metric: Client agrees to the plan and attends the first two check-ins

Pricing and Packaging Review

• Trigger: Discount dependency, downgrade request, or "too expensive" objection
• Action: Analyze the client's actual usage against their current plan. Identify whether they are over-provisioned (opportunity to right-size and retain) or under-utilizing (opportunity to drive adoption to justify cost). Present options that align price to value without setting a precedent for annual discounting.
• Timeline: Within 10 business days
• Success metric: Client agrees to renewal terms without requiring escalation

Competitive Defense

• Trigger: Competitor evaluation detected, competitor mentioned in meetings, RFP activity
• Action: Confirm the competitive threat (which vendor, what stage of evaluation). Pull competitive battle card and identify differentiation points. Arrange executive-to-executive meeting. Offer exclusive pricing, extended terms, or feature roadmap preview. Mobilize internal champion to advocate. Document and address every stated reason for evaluation.
• Timeline: Immediate, within 48 hours of detection
• Success metric: Client agrees to pause evaluation; specific objections are addressed in writing

Medium Priority (Neutral / Monitor)

Proactive Health Check

• Trigger: Health score 60-79 with one or two dimensions below 50
• Action: Schedule a focused check-in to address the weak dimensions. Come prepared with specific observations ("We noticed X has declined -- can we talk about what is driving that?"). Offer targeted resources (training, documentation, peer benchmarks).
• Timeline: Within 15 business days
• Success metric: Weak dimension score improves by 10+ points within 60 days

Feature Adoption Sprint

• Trigger: Low feature adoption despite adequate engagement and satisfaction
• Action: Identify the three highest-value features the client is not using. Create a 30-day adoption sprint with weekly micro-trainings, use-case examples, and progress tracking. Gamify if appropriate (leaderboard, completion badges).
• Timeline: Sprint starts within 10 business days
• Success metric: Client activates all three features within 30 days

Expansion Discovery

• Trigger: Expansion signals detected (usage growth, new department adoption, feature requests for higher tier)
• Action: Schedule a discovery call to understand the client's evolving needs. Map their growth trajectory against the product roadmap. Present an expansion proposal aligned to their next business milestone.
• Timeline: Within 15 business days
• Success metric: Expansion proposal delivered; pipeline created in CRM

Support Recovery

• Trigger: Escalation pattern, critical unresolved issues, negative support sentiment
• Action: Audit all open tickets and escalations. Create priority resolution plan with ETAs. Assign dedicated support contact or escalation manager. Schedule daily standups until critical issues resolved. Communicate proactively on resolution progress. Follow up post-resolution with a satisfaction check.
• Timeline: This week
• Success metric: All critical issues resolved or have committed plan; support CSAT returns to 4.0+

Financial Restructure

• Trigger: Budget cuts, pricing disputes, payment delinquency
• Action: Understand the full financial picture (how severe are the cuts). Model options: reduced scope, phased pricing, payment plans, bridge periods. Present options that preserve the relationship while protecting revenue. Get commitment on a path forward with documented milestones.
• Timeline: Within 15 business days
• Success metric: Agreement on restructured terms; payment plan in place

Churn and Expansion Signal Detection

Beyond the seven dimensions, watch for compound signals that indicate elevated churn risk or expansion potential regardless of individual dimension scores. Cross-reference across dimensions to identify them. Tag each detected signal with its severity and the specific evidence that triggered it.

Churn Signals

High-Severity Churn Signals

These warrant immediate attention. Any one of these should trigger an escalation to CS leadership.

1. Champion Departure + Usage Decline: The primary champion left and usage dropped within 30 days. This is the single strongest predictor of churn. The new stakeholder has no emotional investment in the product and declining usage gives them no reason to build one. 2. Support Escalation + Negative Sentiment: A P0/P1 support ticket was escalated to management and the client's communication tone is hostile or resigned. The client has lost confidence in the ability to resolve their issues. 3. Budget Review + Downgrade Request: The client's finance team initiated a vendor review and the client simultaneously requested pricing concessions or tier downgrade. This signals institutional pressure to cut costs, not just a negotiation tactic. 4. Executive Turnover + Competitive Mention: A new CTO, VP, or department head joined the client and competitors have been mentioned in support tickets, meetings, or survey responses. New leadership often brings vendor preferences from their previous company. 5. Usage Collapse + Non-Response: Usage dropped below 25% of the trailing 90-day average and the client is not responding to outreach. The account may already be lost.

Medium-Severity Churn Signals

These require monitoring and proactive outreach within 1-2 weeks.

6. Flat Renewal + Discount Dependency: The client renewed at the same value only after receiving a discount, for two or more consecutive cycles. The client sees no incremental value and is being retained by price alone. 7. Single-Threaded Relationship: Only one person at the client engages with the team. If that person leaves, the relationship has no continuity. 8. Feature Stagnation: The client has not adopted any new feature in 6+ months despite multiple releases. They are getting less value over time as the product evolves away from their static use case. 9. QBR Decline: The client declined or cancelled the last two QBRs. They do not see enough value in the relationship to invest 30-60 minutes per quarter reviewing it. 10. Survey Non-Response: The client did not respond to the last two NPS or CSAT surveys. Silence is often worse than a low score because it indicates disengagement rather than dissatisfaction.

Expansion Signals

Identify accounts with expansion potential so the forecast includes revenue growth opportunities alongside risk mitigation.

Strong Expansion Signals

1. Usage Exceeding Entitlements: The client is hitting usage caps, requesting higher limits, or being throttled. They need more capacity than they purchased. 2. New Department Adoption: A team or department that was not part of the original purchase has started using the product organically. Cross-functional adoption indicates product-market fit beyond the initial buyer. 3. Unsolicited Feature Requests for Adjacent Capabilities: The client is asking for features that exist in a higher tier, an add-on module, or a product in development. They are trying to solve new problems with the platform. 4. Champion Promotion: The primary champion was promoted to a more senior role. They now have more budget authority and organizational influence to expand the partnership. 5. Proactive Referral or Case Study: The client volunteered to refer other companies or participate in marketing. This level of advocacy strongly correlates with renewal and expansion.

Moderate Expansion Signals

6. Increasing API Usage: API call volume is growing steadily, indicating deeper integration into the client's workflows. Deeper integration increases switching costs and signals long-term commitment. 7. Multi-Stakeholder Engagement: More people at the client are attending meetings, filing tickets, or logging into the product than 6 months ago. Broader engagement means broader organizational dependency. 8. Positive NPS Trend: NPS improved by 2+ points over the last two cycles. Momentum in satisfaction often precedes willingness to expand. 9. Contract Inquiry Before Renewal Window: The client proactively asked about renewal terms, pricing for additional seats, or upgrade options before the renewal window opened. Early interest is a buying signal. 10. Competitive Displacement: The client replaced a competitor's tool for a workflow that was not part of the original deal. Organic competitive wins indicate strong value perception.