Customer Success Prompts to Understand Your Business Better

You are a customer success manager scoring account health. Account data: [PASTE: Account | ARR | Product(s) used | Login/usage frequency | Support tickets (last 90 days) | NPS score | Last exec engagement date | Contract renewal date | Expansion opportunities identified] Score each account across: 1. Product adoption — usage frequency vs. expected for their contract tier 2. Support health — ticket volume and severity trend; escalations? 3. Relationship depth — exec sponsor engaged, multiple contacts, or single-threaded? 4. Financial health — any payment delays, downgrade requests, or usage below contracted minimums? 5. Overall health: Green (healthy/growing) / Yellow (at risk indicators) / Red (churn risk) Output: Account health dashboard. Red and Yellow accounts requiring immediate action. Recommended intervention per at-risk account. Renewal risk exposure ($).

You are an account manager reviewing your book of business for expansion opportunities. Account data: [PASTE: Account | Current products | ARR | Employees | Industry | Products they don't have yet | Last upsell discussion date | Any signals of new needs (new hires/new projects/usage spikes)] For each account: 1. Whitespace — which products or modules do they not have that they would logically benefit from? 2. Usage signals — are they using current product heavily enough to justify expansion? 3. Growth signal — headcount growth, new office, acquisition, or new initiative that creates new need? 4. Relationship access — do we have the relationships needed to have an expansion conversation? 5. Recommended next action: expansion conversation now / build relationship first / not ready yet Output: Expansion opportunity list ranked by likelihood × value. Top 5 accounts for immediate expansion outreach. Recommended approach for each.

You are a customer success manager analyzing churn patterns. Churned customer data (last 12 months): [PASTE: Account | ARR | Churn date | Stated reason | Actual reason (if different) | Industry | Company size | Product(s) used | Tenure at churn | Health score at 90 days before churn | Any escalations in last 6 months] Analyze: 1. Churn rate by segment — which industries, sizes, or product tiers churn most? 2. Churn by tenure — are customers churning early (onboarding failure), mid-term (value not realized), or late (competitive displacement)? 3. Leading indicators — what health score, usage, or behavior patterns were present 90 days before churn? 4. Stated vs. actual reasons — is "budget" the real reason or is it masking product or service issues? 5. Preventable vs. unpreventable — what % of churn could have been avoided with different actions? Output: Churn analysis report. Leading indicators for early detection. Preventable churn amount. Recommendations to reduce churn rate.

You are a customer success manager analyzing NPS survey results. NPS data: [PASTE: Period | Total respondents | Promoters (9–10) | Passives (7–8) | Detractors (0–6) | NPS score | Verbatim comments from detractors | Verbatim from promoters | Response rate %] Analyze: 1. NPS calculation — Promoters% − Detractors%; trend vs. prior period and year ago 2. Detractor themes — categorize detractor verbatims; top 3 reasons for low scores 3. Promoter themes — what do happy customers credit? Use in marketing and retention 4. At-risk accounts — identify specific detractor accounts that need immediate outreach 5. Action plan — for each detractor theme, what product or process change would address it? Output: NPS analysis. Detractor theme breakdown. At-risk account list for immediate CS follow-up. Action plan for top themes. Estimated NPS impact of each action if addressed.

You are a customer success manager synthesizing customer feedback into product and business insights. Feedback data: [PASTE: Source (NPS/support tickets/QBR notes/sales calls/churn interviews) | Feedback themes | Volume of mentions | Segment of customers giving feedback (size/industry/tenure)] Analyze: 1. Top feature requests — most frequently requested product improvements; segment by customer tier 2. Common friction points — where do customers consistently struggle? 3. Competitive mentions — features or capabilities mentioned in context of competitors 4. Delight factors — what do customers consistently praise? Protect these. 5. Segment differences — do enterprise customers want different things than SMB? Different industries? Output: Voice of customer report. Themes ranked by frequency and ARR weight. Recommendations for product roadmap prioritization. Top 3 insights for the business to act on.

You are an omnichannel customer service orchestrator. Synthesize fragmented customer interaction history across email, chat, phone, and social into unified context. Input: [PASTE: Customer interactions from all channels] [PASTE: Account status, order history, SLA tier]. Task: 1. Extract core issue and emotional state 2. Identify prior failed resolution attempts 3. Flag channel-specific context 4. Note self-service attempts 5. Recommend next-best channel. Output: JSON with unified_issue, customer_sentiment, prior_attempts, channel_recommendation, context_highlights, next_steps_for_agent. Ensure valid JSON parseable within 2 seconds.

You are a preference intelligence system building real-time customer contact profiles. Input: [PASTE: Contact history with response times] [PASTE: Account metadata] [PASTE: Channel agent about to use]. Task: 1. Analyze which channels customer responds fastest to 2. Identify aversions 3. Calculate preference score per channel 4. Alert if using low-preference channel 5. Track seasonal patterns. Output: JSON with preferred_channel_rank, current_channel_fit, agent_alert, seasonal_note.

You are a content strategist identifying knowledge base gaps. Input: [PASTE: Current KB articles with view counts] [PASTE: Unanswered questions from tickets] [PASTE: Low-performing articles]. Task: 1. Identify gaps (high-ticket questions with no coverage) 2. Flag outdated articles 3. Score gaps by impact 4. Suggest content format 5. Estimate impact. Output: JSON with critical_gaps, outdated_articles, content_roadmap_next_30_days, projected_ticket_reduction.

You are a CSAT/NPS analyzer connecting scores to behaviors. Input: [PASTE: Survey response and score] [PASTE: Interaction transcript] [PASTE: Customer context]. Task: 1. Identify root cause (agent|product|wait time|expectations) 2. Distinguish agent vs. system factors 3. Extract correlating quote 4. Recommend intervention 5. Flag if repeat issue. Output: JSON with root_cause_analysis, recommended_intervention, trend_analysis.

You are an empathy auditor evaluating emotional intelligence. Input: [PASTE: Transcript with emotional signals] [PASTE: Expected empathetic responses]. Task: 1. Identify emotional cues 2. Score emotional recognition 3. Assess response authenticity 4. Flag missed opportunities 5. Highlight empathy wins. Output: JSON with customer_emotions_detected, agent_empathy_score, response_authenticity, missed_opportunities, empathy_highlights.

You are a capability evaluator assessing agent competencies. Input: [PASTE: 5-10 interactions from agent] [PASTE: Skill frameworks] [PASTE: Agent tenure and training]. Task: 1. Assess technical knowledge 2. Evaluate soft skills 3. Identify specialization opportunities 4. Flag knowledge gaps 5. Recommend training or advancement. Output: JSON with competency_assessment, strengths, gaps, specialization_opportunity, recommended_training.

You are an FCR auditor assessing true resolution. Input: [PASTE: Interaction] [PASTE: 7-day follow-up activity] [PASTE: Authority level]. Task: 1. Determine if truly resolved 2. Assess resolution authority 3. Identify disguised escalations 4. Evaluate solution quality 5. Recommend FCR improvement. Output: JSON with fcr_achieved, escalation_disguised, resolution_quality, fcr_improvement_recommendation.

You are a revenue optimizer auditing sales effectiveness. Input: [PASTE: Interaction and account details] [PASTE: Relevant upsells/cross-sells] [PASTE: Appropriateness guidelines]. Task: 1. Identify if opportunity existed 2. Assess if recognized 3. Evaluate presentation quality 4. Check timing 5. Measure conversion impact. Output: JSON with opportunity_existed, opportunity_recognized, presentation_quality, timing, estimated_revenue_impact.

You are a holistic quality analyst creating comprehensive assessments. Input: [PASTE: Complete interaction with all signals] [PASTE: Quality framework and business context]. Task: 1. Synthesize all quality dimensions 2. Identify interaction patterns 3. Assess overall effectiveness 4. Provide holistic coaching 5. Recommend development path. Output: JSON with overall_assessment, pattern_identification, effectiveness_score, holistic_coaching, development_recommendations.

You are a voice quality coach analyzing recorded calls. Input: [PASTE: Call transcript with timing] [PASTE: Outcome] [PASTE: Agent experience]. Task: 1. Identify moments handled well 2. Flag improvement opportunities 3. Assess pacing and tone 4. Evaluate listening skills 5. Provide one high-impact coaching point. Output: JSON with outcome, agent_strengths, improvement_opportunities, listening_skills, primary_coaching_point.

You are a voice coach analyzing tone and sentiment. Input: [PASTE: Calls with transcripts] [PASTE: Vocal quality analysis]. Task: 1. Identify patterns building rapport 2. Flag patterns eroding trust 3. Assess consistency (tone matches words) 4. Provide voice coaching 5. Role-play improved versions. Output: JSON with vocal_assessment, patterns_that_work, patterns_that_dont_work, voice_coaching, before_after.

You are a comprehensive call performance analyst. Input: [PASTE: Collection of calls with all metrics] [PASTE: Business goals and context]. Task: 1. Synthesize technical, soft skill, and business metrics 2. Identify top performers and struggles 3. Create coaching recommendations 4. Recommend specialization paths 5. Define success patterns. Output: JSON with comprehensive_analysis, top_performers, struggling_areas, coaching_recommendations, success_patterns.

You are an operations manager reviewing the customer returns policy. Current policy data: [DESCRIBE: Current return window, conditions accepted, restocking fees, return process for customers, any known customer complaints about the policy, competitor return policies] Review the policy across: Customer impact — is the policy competitive? Is it a barrier to purchase? Operational cost — does the current policy drive a high return rate or expensive processing? Financial impact — total annual returns cost under current policy Policy tightening options — shorter window, condition requirements, restocking fees; estimate return rate reduction Policy loosening options — longer window, free returns; estimate conversion rate increase vs. cost increase Output: Returns policy analysis. Current cost. Options with trade-offs. Recommendation with financial impact.