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Analysisgpt-4o

Analyze Cancellation Reasons

Prompt
You are a customer success analyst examining why customers cancel. Extract patterns and actionable insights from cancellation feedback.

**Cancellation Data:**
{{cancellation_data}}

**Product Context:**
{{product_context}}

**Time Period:**
{{time_period}}

Analyze cancellation reasons:

---

## Executive Summary

**Cancellation volume:** [X cancellations this period]
**MRR impact:** [Revenue lost]
**Top reason:** [Primary driver]
**Biggest opportunity:** [Most actionable insight]

---

## Cancellation Breakdown

### By Reason
| Reason | Count | % | MRR Lost | Trend |
|--------|-------|---|----------|-------|
| [Reason 1] | X | X% | $X | ↑/↓/→ |
| [Reason 2] | X | X% | $X | ↑/↓/→ |

### By Customer Segment
| Segment | Cancellations | Churn Rate | Primary Reason |
|---------|---------------|------------|----------------|
| [Segment] | X | X% | [Reason] |

### By Tenure
| Tenure | Cancellations | % of Total |
|--------|---------------|------------|
| 0-30 days | X | X% |
| 31-90 days | X | X% |
| 91-180 days | X | X% |
| 180+ days | X | X% |

---

## Reason Deep Dive

### Reason 1: [Name] (X%)

**Description:** [What this category means]

**Representative feedback:**
> "[Quote 1]"
> "[Quote 2]"
> "[Quote 3]"

**Root cause analysis:**
- [Underlying issue]
- [Contributing factors]

**Who this affects:**
- [Customer segment pattern]

**Preventability:** Preventable / Partially preventable / Unpreventable

**Recommended actions:**
1. [Specific action]
2. [Specific action]

---

### Reason 2: [Name] (X%)
[Same structure...]

---

## Patterns & Insights

### Early Warning Signs
Behaviors that preceded cancellation:
- [Signal 1] — [X days before cancellation]
- [Signal 2] — [X days before cancellation]

### Preventable vs. Unpreventable
| Category | Count | % |
|----------|-------|---|
| Preventable (product/service) | X | X% |
| Partially preventable | X | X% |
| Unpreventable (business closed, etc.) | X | X% |

### Competitor Mentions
- [Competitor]: [X mentions, what they're offering]

---

## Recommendations

### Immediate Actions (This Week)
| Action | Impact | Effort | Owner |
|--------|--------|--------|-------|
| [Action] | High/Med/Low | Low/Med/High | [Team] |

### Process Improvements
| Change | Prevents | Implementation |
|--------|----------|----------------|
| [Change] | [Reason it prevents] | [How to implement] |

### Product Improvements
| Feature/Fix | Cancellation Reason | Effort | Priority |
|-------------|---------------------|--------|----------|
| [Feature] | [What it addresses] | [T-shirt size] | [P1/P2/P3] |

---

## Win-Back Opportunities

| Segment | Approach | Timing | Expected Win Rate |
|---------|----------|--------|-------------------|
| [Segment] | [Outreach strategy] | [When to contact] | [%] |

---

## Tracking Improvements

Metrics to monitor:
- [Metric] — Target: [Goal]
- [Metric] — Target: [Goal]

---

Focus on preventable cancellations. Every cancellation is learning if you extract the right insights.
Example

Input

Data: 45 cancellations last month with exit survey responses
Product: Email marketing SaaS, recently increased prices
Period: January 2024

Output

## Executive Summary

**Cancellation volume:** 45 cancellations (up 30% MoM)
**MRR impact:** $4,200 lost
**Top reason:** Pricing (40%)
**Biggest opportunity:** Introduce a lower-tier plan to retain price-sensitive SMBs...