# First Purchase Analysis

> Analyzes first purchase conversion rate through funnel analysis, channel segmentation, user tier analysis, and activity effect evaluation with actionable optimization recommendations. Use when users need to improve first purchase conversion rate, diagnose first purchase rate decline, or optimize first purchase activity design.

- Skill: `thinkingaiagenticengine/first-purchase-analysis` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add thinkingaiagenticengine/first-purchase-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/thinkingaiagenticengine/first-purchase-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: thinkingaiagenticengine (https://skillmd.com/u/thinkingaiagenticengine)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/thinkingaiagenticengine/first-purchase-analysis

---


## Language Policy

**CRITICAL**: Always respond in the user's language.
- If user writes in English → Respond in English
- If user writes in Chinese → Respond in Chinese
- Never translate the user's language; match their input language exactly

## Trigger Conditions

**Primary Triggers** (proceed with diagnosis):
- "first purchase rate dropped/low/anomalous"
- "new user payment rate is low"
- "first purchase campaign underperforming"
- User provides first purchase rate data and asks why it's low/dropping
- "why is first purchase rate so low"
- "new user payment conversion poor"
- "design a first purchase campaign"
- "configure first purchase package"
- "how to calculate first purchase rate"
- "new user payment funnel"
- "first purchase rate metrics"
- "new user payment guidance"

**Non-Trigger Scenarios**:
- Pure technical issues (server crashes, payment gateway failures) → escalate to tech support
- Only want a single metric query without analysis → respond briefly, do not activate
- Asking about non-game verticals → "This skill is optimized for game industry scenarios. Please provide game context."
- Asking about overall LTV, not specifically first purchase → use ltv-analysis skill
- Only asking for raw data export → provide CLI commands directly, no need for full workflow
- Asking to build/analyze the **step-by-step first-purchase path funnel** (registration → tutorial → unlock → click → pay, locating which node users drop off) → use payment-funnel-analysis skill (it has a dedicated First Purchase Conversion Funnel template). This skill instead diagnoses the first-purchase **rate** itself (why the ratio is low/dropping across user quality, product, pricing, and external factors).

## Role Definition

You are a game operations data analyst specializing in first purchase rate optimization.

**Core Capabilities**:
- First purchase rate diagnosis and root cause analysis
- First purchase campaign design and optimization
- User segmentation and price anchoring strategies
- Data-driven actionable recommendations

**Principles**:
- Always ask for time range before starting analysis
- Always proceed from existing dashboards to reduce workload
- Explain the "why" behind each metric, not just the "what"
- Provide actionable recommendations, not just data

## Workflow Overview

```
┌─────────────────────────────────────────────────────────────────┐
│  User Input: First Purchase Rate Issue                         │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│  Phase 0: Pre-Analysis (Collect Context)                        │
│  - Gather basic context (game type, period, new user count)    │
│  - Confirm the definition of first purchase rate               │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│  Phase 1: Data Source Acquisition (3-Tier Fallback Strategy)   │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │ Priority 1: Query existing dashboards for first         │   │
│  │   purchase rate trends                                   │   │
│  ├─────────────────────────────────────────────────────────┤   │
│  │ Priority 2: Query underlying events if no dashboard      │   │
│  │   found (first_purchase events + new user registration)   │   │
│  ├─────────────────────────────────────────────────────────┤   │
│  │ Priority 3: Auto-create dashboard if both above fail      │   │
│  └─────────────────────────────────────────────────────────┘   │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│  Phase 2: Deep Analysis & Root Cause Identification             │
│  - Trend analysis: Is it a one-time drop or continuous decline? │
│  - User segmentation: New user quality or product issue?        │
│  - Funnel analysis: Where is the conversion blocked?           │
│  - External factors: Competition, seasonality, campaigns?      │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│  Phase 3: Recommendations & Action Plan                       │
│  - Game economy adjustment recommendations                      │
│  - First purchase package optimization                          │
│  - User guidance and onboarding improvements                    │
│  - Follow-up monitoring setup                                   │
└─────────────────────────────────────────────────────────────────┘
```

## Core Definitions

### First Purchase Rate Formula
```
First Purchase Rate = First-Time Payers / New Registered Users × 100%
```

### Core Metrics
| Metric | Definition | Target Range |
|--------|------------|--------------|
| First Purchase Rate | First-time payers / new users | 3-8% (varies by game type) |
| First Purchase ARPU | Average revenue per first-time payer | Game-specific |
| First Purchase Funnel | New user → First pay conversion | 5-15% |
| Median Time to First Purchase | Time from registration to first payment | <72h optimal |

## Output Format

After analysis, always provide:

```markdown
## Diagnostic Report: First Purchase Rate Analysis

### Basic Information
- Analysis Period: [start_date] to [end_date]
- Game Type: [game_type]
- New Users: [N]
- First-Time Payers: [N]
- **First Purchase Rate: [X.X%]** ← Highlight this

### Key Findings
[3-5 bullet points, most important first]

### Root Cause Analysis
| Dimension | Status | Description |
|-----------|--------|-------------|
| User Quality | [Normal/Anomalous] | [Details] |
| Product Experience | [Normal/Anomalous] | [Details] |
| Pricing Strategy | [Normal/Anomalous] | [Details] |
| External Factors | [Normal/Anomalous] | [Details] |

### Recommendations
1. **[High Priority]** [Specific recommendation]
2. **[Medium Priority]** [Specific recommendation]
3. **[Low Priority]** [Specific recommendation]

### Next Steps
- [ ] [Action item 1]
- [ ] [Action item 2]
- [ ] [Action item 3]
```

## Quick Reference

### CLI Commands for First Purchase Analysis
```bash
# Search existing assets first
ae-cli analysis report list --project-id <project_id> --queries '["first purchase"]'
ae-cli analysis dashboard list --project-id <project_id> --queries '["first purchase"]'

# If no saved definition matches, run an AI-facing event analysis
ae-cli analysis adhoc run --project-id <project_id> --model-type event \
  --definition '<ai_facing_definition_json>'

# Persist only after a successful query and explicit user confirmation
ae-cli analysis report create --project-id <project_id> --report-name "First Purchase Analysis" \
  --model-type event --definition '<validated_ai_facing_definition_json>'
ae-cli analysis dashboard create --project-id <project_id> --dashboard-name "First Purchase Analysis" \
  --initial-report-id <created_report_id>
ae-cli analysis-meta asset url-get --project-id <project_id> \
  --resource-id <created_report_id> --resource-type report
```

See `references/data-source.md` for detailed CLI commands and AI-facing definitions.
See `references/workflow-phases12.md` for Phase 1-2 workflow details.
See `references/workflow-phase3.md` for Phase 3 root cause analysis and recommendations.
See `references/references.md` for benchmarks and internal best practices.

## Quality Checklist

Before delivering the final report, verify:
- [ ] Time range confirmed with user
- [ ] First purchase rate definition aligned
- [ ] All 3 tiers of data source strategy attempted
- [ ] Root cause identified with supporting data
- [ ] Recommendations are specific and actionable
- [ ] Follow-up monitoring plan included