Growth Analytics And Dashboard Management
by ShunsukeHayashi
KPI framework setup, dashboard design, cohort analysis, and data-driven decision making. Use when analyzing growth metrics, building KPI dashboards, or implementing analytics systems.
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name: Growth Analytics and Dashboard Management description: KPI framework setup, dashboard design, cohort analysis, and data-driven decision making. Use when analyzing growth metrics, building KPI dashboards, or implementing analytics systems. allowed-tools: Read, Write, WebFetch, Bash
📊 Growth Analytics and Dashboard Management
Version: 2.0.0 Last Updated: 2025-11-22 Priority: ⭐⭐⭐⭐ (P1 Level - Business) Purpose: KPIフレームワーク、ダッシュボード設計、データドリブン意思決定
📋 概要
20以上のメトリクスによるKPIフレームワーク、ダッシュボード設計、 コホート分析、予測分析を通じたグロース支援を提供します。
🎯 P0: 呼び出しトリガー
| トリガー | 例 |
|---|---|
| メトリクス分析 | "analyze our growth metrics" |
| CAC/LTV | "what's our CAC/LTV?" |
| ダッシュボード | "build a KPI dashboard" |
| データ分析 | "data-driven decisions" |
| コホート | "cohort analysis" |
🔧 P1: KPIカテゴリ一覧
5カテゴリ・20+メトリクス
| カテゴリ | メトリクス | 優先度 | 測定頻度 |
|---|---|---|---|
| Acquisition | CAC, Traffic, Conversion | 高 | 週次 |
| Activation | Time-to-Value, Onboarding Rate | 高 | 週次 |
| Revenue | MRR, ARPU, LTV | 高 | 月次 |
| Retention | Churn, NRR, DAU/MAU | 高 | 月次 |
| Referral | NPS, Viral Coefficient | 中 | 四半期 |
🚀 P2: ダッシュボード設計
Dashboard Types
| Type | 対象 | 更新頻度 | メトリクス数 |
|---|---|---|---|
| Executive | 経営層 | 週次 | 5-7 |
| Product | PM/開発 | 日次 | 10-15 |
| Marketing | マーケ | 日次 | 8-12 |
| Sales | 営業 | リアルタイム | 6-10 |
Pattern 1: Executive Dashboard
┌─────────────────────────────────────────────┐
│ Executive Dashboard │
├─────────────┬─────────────┬─────────────────┤
│ MRR │ Churn │ NPS │
│ ¥XXX万 │ 2.1% │ 42 │
│ ↑12% MoM │ ↓0.3% │ ↑5 pts │
├─────────────┼─────────────┼─────────────────┤
│ CAC │ LTV │ LTV/CAC │
│ ¥8,500 │ ¥85,000 │ 10.0x │
│ ↓5% │ ↑8% │ ↑1.2x │
└─────────────┴─────────────┴─────────────────┘
Pattern 2: Product Dashboard
Metrics:
- DAU/MAU (Stickiness)
- Feature Adoption Rate
- Time-in-App
- Error Rate
- Page Load Time
- User Journey Completion
⚡ P3: 分析手法
Cohort Analysis
| 月 | Week 1 | Week 2 | Week 3 | Week 4 |
|---|---|---|---|---|
| Jan | 100% | 65% | 52% | 48% |
| Feb | 100% | 68% | 55% | 51% |
| Mar | 100% | 72% | 58% | 54% |
解釈: リテンション改善トレンド（+6% W4）
Funnel Analysis
Awareness : 10,000 (100%)
↓
Interest : 3,000 (30%) ← Drop: 70%
↓
Evaluation : 1,200 (12%) ← Drop: 60%
↓
Trial : 600 (6%) ← Drop: 50%
↓
Purchase : 300 (3%) ← Drop: 50%
改善ポイント: Interest→Evaluation (60% drop)
A/B Testing Framework
| 要素 | 内容 |
|---|---|
| 仮説 | 「CTA色変更で+10% CVR」 |
| サンプルサイズ | 1,000 per variant |
| 期間 | 2週間 |
| 成功基準 | p < 0.05, +5% CVR |
📊 PDCA サイクル
4週間スプリント
| 週 | フェーズ | アクション |
|---|---|---|
| Week 1 | Plan | KPI設定、仮説立案 |
| Week 2 | Do | 施策実行、データ収集 |
| Week 3 | Check | 分析、結果評価 |
| Week 4 | Act | 改善、次サイクル準備 |
🛡️ 予測分析
Churn Prediction
リスクスコア =
ログイン頻度低下 × 0.3 +
機能利用減少 × 0.25 +
サポート問い合わせ × 0.2 +
契約更新近接 × 0.15 +
決済失敗履歴 × 0.1
| スコア | リスク | アクション |
|---|---|---|
| 0-30 | 低 | 通常対応 |
| 31-60 | 中 | プロアクティブ連絡 |
| 61-100 | 高 | 緊急介入 |
Revenue Forecasting
予測MRR =
現在MRR × (1 - Churn%) +
New MRR (リード × CVR × ARPU) +
Expansion MRR (アップセル対象 × Rate)
✅ 成功基準
| メトリクス | 目標 | 測定 |
|---|---|---|
| LTV/CAC | >3.0x | 月次 |
| Churn | <5% | 月次 |
| NPS | >40 | 四半期 |
| DAU/MAU | >30% | 週次 |
🔗 関連Skills
- Market Research: 市場データ収集
- Sales CRM: 営業メトリクス
- Content Marketing: マーケティングKPI
- Business Strategy: 戦略立案との連携
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