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growth-analytics-dashboard skill

/miyabi-full/skills/growth-analytics-dashboard

This skill helps you design KPI dashboards, analyze growth metrics, and drive data-driven decisions with cohort and forecast insights.

npx playbooks add skill shunsukehayashi/miyabi-claude-plugins --skill growth-analytics-dashboard

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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

```yaml
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

Overview

This skill helps teams set up a growth analytics framework, design KPI dashboards, and run cohort and funnel analyses to enable data-driven decisions. It focuses on measurable outcomes like CAC/LTV, retention, and MRR while providing repeatable processes for experimentation and forecasting. Use it to align stakeholders on metrics and to operationalize growth work across product, marketing, and executive audiences.

How this skill works

The skill defines a 5-category KPI framework (Acquisition, Activation, Revenue, Retention, Referral) and recommends metric frequency and priority. It prescribes dashboard patterns for executive, product, marketing, and sales audiences, plus analyses: cohort retention, funnel breakdowns, A/B testing, and churn/revenue forecasting. It also embeds a 4-week PDCA sprint to iterate on hypotheses and actions.

When to use it

Design a KPI dashboard for execs, product, marketing, or sales
Run cohort or funnel analysis to diagnose retention or conversion drops
Estimate CAC, LTV, and LTV/CAC to validate unit economics
Set up A/B tests and interpret results against statistical criteria
Build churn or revenue forecasts for planning and prioritization

Best practices

Start with a small set of high-priority KPIs aligned to business goals (5–10 metrics)
Design separate dashboards per audience with tailored cadence and metric count
Instrument events and sources consistently to enable cohort and funnel analysis
Use a PDCA sprint (4 weeks) to structure experiments and measure impact
Adopt clear success criteria for tests (sample size, duration, p-value, minimum lift)
Translate risk scores and forecasts into concrete playbooks for follow-up actions

Example use cases

Build an executive dashboard showing MRR, Churn, NPS, CAC, and LTV/CAC updated weekly
Diagnose a 60% drop between Interest and Evaluation with funnel analysis and prioritize fixes
Run cohort retention tracking to measure onboarding changes and report W4 improvement
Design an A/B test for CTA changes with defined sample size and success thresholds
Implement churn prediction scoring to trigger proactive outreach for high-risk users

FAQ

What cadence should different dashboards use?

Executive: weekly; Product and Marketing: daily; Sales: near real-time. Metric freshness should match audience decision cycles.

What success criteria should I set for A/B tests?

Define hypothesis, required sample size per variant, test duration (typical 1–2 weeks), and statistical threshold (commonly p < 0.05 and minimum relative lift, e.g., +5% CVR).