# ============================================ # セグメント別KPI設計システム # ============================================ # セグメント別KPIとは？ # → 顧客グループごとに異なる目標を設定 # → VIP顧客、成長顧客、休眠顧客で別々のKPIを持つ # → 施策の効果測定が明確になる import pandas as pd import numpy as np class SegmentKPIDesigner: “”” セグメント別KPIを設計するクラス 使い方: 1. セグメントを定義（define_segment） 2. 各セグメントにKPIを設定（set_kpi） 3. レポートを生成（generate_report） “”” def __init__(self): self.segments = {} # セグメント情報を格納 self.kpis = {} # KPI情報を格納 def define_segment(self, name, characteristics, size, current_metrics): “”” セグメントを定義 Parameters: ———– name : str – セグメント名（例: “VIP顧客”） characteristics : str – セグメントの特徴 size : int – 顧客数 current_metrics : dict – 現在の指標値 “”” self.segments[name] = { ‘characteristics’: characteristics, ‘size’: size, ‘current’: current_metrics } def set_kpi(self, segment_name, kpi_name, current, target, timeframe, priority): “”” セグメント別KPIを設定 Parameters: ———– segment_name : str – 対象セグメント名 kpi_name : str – KPI名（例: “解約率”） current : float – 現在値 target : float – 目標値 timeframe : str – 達成期間（例: “6ヶ月”） priority : int – 優先度（1が最高） “”” if segment_name not in self.kpis: self.kpis[segment_name] = [] # 改善率を計算（目標が現在より低い場合はマイナス） improvement = ((target – current) / current) * 100 self.kpis[segment_name].append({ ‘kpi_name’: kpi_name, ‘current’: current, ‘target’: target, ‘improvement’: improvement, ‘timeframe’: timeframe, ‘priority’: priority }) def generate_report(self): “”” KPIレポートを生成 登録されたすべてのセグメントとKPIを 見やすい形式で出力する “”” print(“=” * 70) print(“セグメント別KPI設計レポート”) print(“=” * 70) # .items(): 辞書のキーと値をペアで取得 for seg_name, seg_info in self.segments.items(): print(f”\n{‘─’ * 70}”) print(f”◆ {seg_name}”) print(f”{‘─’ * 70}”) print(f”特徴: {seg_info[‘characteristics’]}”) print(f”顧客数: {seg_info[‘size’]:,}人”) # このセグメントにKPIが設定されている場合 if seg_name in self.kpis: print(f”\n【KPI一覧】”) # sorted(): 優先度順にソート # key=lambda x: x[‘priority’]: ソートキーを指定 for kpi in sorted(self.kpis[seg_name], key=lambda x: x[‘priority’]): print(f”\n {kpi[‘kpi_name’]}:”) print(f” 現状: {kpi[‘current’]:,.1f}”) print(f” 目標: {kpi[‘target’]:,.1f}”) # :+.1f: 正の数には+を表示 print(f” 改善率: {kpi[‘improvement’]:+.1f}%”) print(f” 期間: {kpi[‘timeframe’]}”) # ‘★’ * n: 文字列をn回繰り返し print(f” 優先度: {‘★’ * kpi[‘priority’]}”) # ============================================ # 実行例：セグメント別KPI設計 # ============================================ # 1. インスタンスを作成 designer = SegmentKPIDesigner() # ============================================ # 2. セグメントを定義 # ============================================ # 各セグメントの特徴と規模を登録 # VIP顧客：最も価値の高い顧客層 designer.define_segment( name=”VIP顧客”, characteristics=”高LTV、低解約率、高頻度購入”, size=500, current_metrics={‘ltv’: 500000, ‘churn’: 2, ‘frequency’: 12} ) # 成長顧客：VIPに育てたい顧客層 designer.define_segment( name=”成長顧客”, characteristics=”中LTV、伸びしろあり、アクティブ”, size=2000, current_metrics={‘ltv’: 150000, ‘churn’: 5, ‘frequency’: 6} ) # 休眠顧客：再活性化したい顧客層 designer.define_segment( name=”休眠顧客”, characteristics=”過去購入あり、最近アクティブでない”, size=3000, current_metrics={‘ltv’: 50000, ‘churn’: 15, ‘frequency’: 1} ) # ============================================ # 3. セグメント別にKPIを設定 # ============================================ # set_kpi(セグメント名, KPI名, 現状値, 目標値, 期間, 優先度) # VIP顧客のKPI：維持と拡大が目的 designer.set_kpi(“VIP顧客”, “解約率”, 2.0, 1.5, “6ヶ月”, 1) # 最優先 designer.set_kpi(“VIP顧客”, “客単価”, 50000, 60000, “12ヶ月”, 2) designer.set_kpi(“VIP顧客”, “紹介数”, 0.5, 1.0, “12ヶ月”, 3) # 成長顧客のKPI：VIPへの育成が目的 designer.set_kpi(“成長顧客”, “購買頻度”, 6, 8, “6ヶ月”, 1) # 最優先 designer.set_kpi(“成長顧客”, “LTV”, 150000, 200000, “12ヶ月”, 2) designer.set_kpi(“成長顧客”, “解約率”, 5.0, 4.0, “6ヶ月”, 3) # 休眠顧客のKPI：再活性化が目的 designer.set_kpi(“休眠顧客”, “復活率”, 5.0, 15.0, “3ヶ月”, 1) # 最優先 designer.set_kpi(“休眠顧客”, “再購入金額”, 10000, 20000, “6ヶ月”, 2) # ============================================ # 4. レポート生成 # ============================================ designer.generate_report()

# 施策の優先順位付けシステム class StrategyPrioritizer: “””施策の優先順位を決定するクラス””” def __init__(self): self.strategies = [] def add_strategy(self, segment, name, impact_score, effort_score, expected_revenue, cost, timeframe): “””施策を追加（impact/effortは1-10のスコア）””” roi = ((expected_revenue – cost) / cost) * 100 if cost > 0 else 0 # 優先度スコア = Impact / Effort priority_score = impact_score / effort_score # 象限の判定 if impact_score >= 6 and effort_score <= 5: quadrant = "Quick Wins" recommendation = "最優先で実施" elif impact_score >= 6 and effort_score > 5: quadrant = “Major Projects” recommendation = “計画的に実施” elif impact_score < 6 and effort_score <= 5: quadrant = "Fill-ins" recommendation = "余裕があれば" else: quadrant = "Thankless Tasks" recommendation = "実施を見送り" self.strategies.append({ 'segment': segment, 'name': name, 'impact': impact_score, 'effort': effort_score, 'expected_revenue': expected_revenue, 'cost': cost, 'roi': roi, 'priority_score': priority_score, 'quadrant': quadrant, 'recommendation': recommendation, 'timeframe': timeframe }) def get_prioritized_list(self): """優先度順にソートしたリストを返す""" return sorted(self.strategies, key=lambda x: x['priority_score'], reverse=True) def generate_action_plan(self): """アクションプランを生成""" print("=" * 70) print("施策優先順位アクションプラン") print("=" * 70) # 象限別に分類 quadrants = { "Quick Wins": [], "Major Projects": [], "Fill-ins": [], "Thankless Tasks": [] } for s in self.strategies: quadrants[s['quadrant']].append(s) # 象限ごとに表示 for quadrant, strategies in quadrants.items(): if strategies: print(f"\n{'─' * 70}") print(f"【{quadrant}】") print(f"{'─' * 70}") for s in sorted(strategies, key=lambda x: x['priority_score'], reverse=True): print(f"\n◆ [{s['segment']}] {s['name']}") print(f" Impact: {s['impact']}/10 Effort: {s['effort']}/10") print(f" 期待収益: ¥{s['expected_revenue']:,.0f}") print(f" コスト: ¥{s['cost']:,.0f}") print(f" ROI: {s['roi']:.0f}%") print(f" 期間: {s['timeframe']}") print(f" → {s['recommendation']}") # サマリー total_revenue = sum(s['expected_revenue'] for s in self.strategies if s['quadrant'] in ['Quick Wins', 'Major Projects']) total_cost = sum(s['cost'] for s in self.strategies if s['quadrant'] in ['Quick Wins', 'Major Projects']) print(f"\n{'=' * 70}") print("【実施推奨施策のサマリー】") print(f" 対象施策数: {len([s for s in self.strategies if s['quadrant'] in ['Quick Wins', 'Major Projects']])}件") print(f" 期待総収益: ¥{total_revenue:,.0f}") print(f" 総コスト: ¥{total_cost:,.0f}") print(f" 総合ROI: {((total_revenue-total_cost)/total_cost)*100:.0f}%") # 実行例 prioritizer = StrategyPrioritizer() # VIP顧客向け施策 prioritizer.add_strategy( segment="VIP顧客", name="専用コンシェルジュサービス", impact_score=8, effort_score=4, expected_revenue=5000000, cost=1000000, timeframe="3ヶ月" ) prioritizer.add_strategy( segment="VIP顧客", name="限定商品先行販売", impact_score=7, effort_score=3, expected_revenue=3000000, cost=500000, timeframe="1ヶ月" ) # 成長顧客向け施策 prioritizer.add_strategy( segment="成長顧客", name="ポイント2倍キャンペーン", impact_score=6, effort_score=2, expected_revenue=8000000, cost=2000000, timeframe="1ヶ月" ) prioritizer.add_strategy( segment="成長顧客", name="パーソナライズドレコメンド", impact_score=8, effort_score=7, expected_revenue=15000000, cost=5000000, timeframe="6ヶ月" ) # 休眠顧客向け施策 prioritizer.add_strategy( segment="休眠顧客", name="復活割引クーポン", impact_score=5, effort_score=2, expected_revenue=2000000, cost=500000, timeframe="1ヶ月" ) prioritizer.add_strategy( segment="休眠顧客", name="大規模リエンゲージメント", impact_score=4, effort_score=8, expected_revenue=3000000, cost=2000000, timeframe="6ヶ月" ) # アクションプラン生成 prioritizer.generate_action_plan()

# セグメント別投資配分の最適化 def calculate_optimal_allocation(segments, total_budget): “”” セグメント別の最適投資配分を計算 ROIが高いセグメントに優先的に配分 “”” print(“=” * 60) print(“セグメント別投資配分分析”) print(“=” * 60) print(f”\n総予算: ¥{total_budget:,.0f}”) # ROI順にソート sorted_segments = sorted(segments, key=lambda x: x[‘expected_roi’], reverse=True) # 基本配分（ROI比例） total_roi = sum(s[‘expected_roi’] for s in segments) print(“\n【セグメント別分析】”) print(“-” * 60) allocations = [] for seg in sorted_segments: # ROI比例配分 base_allocation = (seg[‘expected_roi’] / total_roi) * total_budget # 顧客数を考慮した調整 # 大きいセグメントには最低限の配分を保証 min_allocation = seg[‘customer_count’] * 100 # 1人あたり100円最低 final_allocation = max(base_allocation, min_allocation) per_customer = final_allocation / seg[‘customer_count’] expected_return = final_allocation * (1 + seg[‘expected_roi’]/100) allocations.append({ ‘name’: seg[‘name’], ‘allocation’: final_allocation, ‘per_customer’: per_customer, ‘expected_return’: expected_return, ‘roi’: seg[‘expected_roi’] }) print(f”\n◆ {seg[‘name’]}”) print(f” 顧客数: {seg[‘customer_count’]:,}人”) print(f” 期待ROI: {seg[‘expected_roi’]}%”) print(f” 配分額: ¥{final_allocation:,.0f}”) print(f” 1人あたり: ¥{per_customer:,.0f}”) print(f” 期待リターン: ¥{expected_return:,.0f}”) # 配分調整（総予算に合わせる） total_allocated = sum(a[‘allocation’] for a in allocations) adjustment_ratio = total_budget / total_allocated print(“\n” + “=” * 60) print(“【最終配分】”) print(“-” * 60) total_return = 0 for a in allocations: adjusted = a[‘allocation’] * adjustment_ratio adjusted_return = adjusted * (1 + a[‘roi’]/100) total_return += adjusted_return pct = (adjusted / total_budget) * 100 print(f”{a[‘name’]}: ¥{adjusted:,.0f} ({pct:.1f}%)”) overall_roi = ((total_return – total_budget) / total_budget) * 100 print(f”\n期待総リターン: ¥{total_return:,.0f}”) print(f”総合ROI: {overall_roi:.1f}%”) return allocations # 実行例 segments = [ { ‘name’: ‘VIP顧客’, ‘customer_count’: 500, ‘expected_roi’: 400 # 400%のROI }, { ‘name’: ‘成長顧客’, ‘customer_count’: 2000, ‘expected_roi’: 200 # 200%のROI }, { ‘name’: ‘一般顧客’, ‘customer_count’: 5000, ‘expected_roi’: 100 # 100%のROI }, { ‘name’: ‘休眠顧客’, ‘customer_count’: 3000, ‘expected_roi’: 50 # 50%のROI } ] allocations = calculate_optimal_allocation(segments, 10000000)