From 583e5eac23a1f2c193d6382d53c20b4a05de1258 Mon Sep 17 00:00:00 2001 From: tak-ka3 Date: Thu, 9 Oct 2025 20:08:59 +0900 Subject: [PATCH 1/5] feat: add advanced tutorial --- .../cross_table/inputs/bank_input/data.csv | 1001 +++++++++++++++++ .../inputs/luxury_watch_input/data.csv | 1001 +++++++++++++++++ ...dvanced_apc_cli_tutorial_cross_table.ipynb | 47 + 3 files changed, 2049 insertions(+) create mode 100644 functions/cross_table/inputs/bank_input/data.csv create mode 100644 functions/cross_table/inputs/luxury_watch_input/data.csv create mode 100644 notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb diff --git a/functions/cross_table/inputs/bank_input/data.csv b/functions/cross_table/inputs/bank_input/data.csv new file mode 100644 index 0000000..81f3245 --- /dev/null +++ b/functions/cross_table/inputs/bank_input/data.csv @@ -0,0 +1,1001 @@ +hashed_email,account_balance,monthly_income,credit_score +4cdde47d052101fe3de4e3659acc6dea,High,High,Low 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+b00d0b66e8cb9f3d6797999d4e2bf6bb,Medium,High,Low +bb5055536360a7374a1fa0006129ffe3,High,Low,High +4c08000cee32c23526b3317d4ffed0fa,Low,High,Medium +d9257247c2370e370de46cb3cd01f432,High,High,Low +ef7ba5cfeb65e286e8d7eef997916abe,Low,High,High +78a50fc928f9977a93dee25178b073a3,Medium,High,High +86c2dcdf24ebdfe42afe00c2a4a0bca0,High,Low,High +a0aa4385da8d6e1d1002ed76c10b1867,High,Medium,High +f433dc451ac47b49a7ea6c60d3e9e71e,High,Medium,Medium +75135d4d0040926a9a6933ef24e08e11,High,Low,Low +2c9a5fe825087e1a5eac11fc640f3c04,Medium,Medium,High +22bb3e099fa5dde25f06350411093cae,Low,Medium,Low +b42e03520995d058134d2b8ce527a846,Low,Medium,Low +0d8a0fbdbd2542059817d3c438a077e3,High,Medium,High +fe14d4f3ec439eec6b887802f0f17e3c,Medium,Medium,High +d00d44caccb4dc8d3be93c0dcb32c8f1,Low,High,Low +e701e312e3f8d54fe3f9cc33af158c84,Low,Low,Low +cc4b68b5c430131995906404f1e1e59e,Low,Medium,Low +cded961813308bba65f40e0b82dbbd83,Low,Medium,Medium +f934dd43131f49b67bc87fe36038644d,Low,Medium,Medium 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b/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb new file mode 100644 index 0000000..e0289f7 --- /dev/null +++ b/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb @@ -0,0 +1,47 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# 応用ユースケース：金融機関と高級時計会社のクロス集計分析\n\nこのチュートリアルでは、AutoPrivacy DCRを使用して、金融機関（銀行）と高級時計会社が保持するデータを安全に突合し、顧客の金融属性と購買行動を分析する応用例を紹介します。\n\n**重要**: このチュートリアルは、[基本チュートリアル](./basic_apc_cli_tutorial.ipynb)の実行手順を理解していることを前提としています。APC-CLIの具体的なコマンドや実行方法については基本チュートリアルを参照してください。\n\n---\n\n## ユースケースの背景\n\n企業が単独で保有するデータには限界があります：\n\n- **銀行** → 顧客の「収入・資産・信用度」などの金融的な側面は分かるが、**具体的にどんなモノにお金を使っているか**までは分からない\n- **高級時計会社** → 「どの顧客が時計を買ったか」「どのラインに興味があるか」は分かるが、その顧客の**収入水準や資産規模、信用力**は分からない\n\n## 解決策\n\n**AutoPrivacy DCR上でIDを元に安全に同意なしで突合することで「金融属性 × 購買実績」のクロス集計が可能になり、マーケティングROIを高められる**\n\n異なる企業のデータを組み合わせることで、単独では見えなかった顧客の全体像が明らかになります。" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## データ概要\n\n### 銀行データ（入力1）\n- **account_balance**: 口座残高レベル (High/Medium/Low)\n- **monthly_income**: 月収レベル (High/Medium/Low) \n- **credit_score**: 信用スコア (High/Medium/Low)\n\n### 高級時計会社データ（入力2）\n- **total_purchase_amount**: 累計購入額 (High/Medium/Low)\n- **last_purchase_date**: 最終購入日からの経過 (Within_1year/1-3years/Over_3years)\n- **store_visits_12months**: 過去12か月の来店回数 (High/Medium/Low)\n- **product_category_interest**: 関心商品カテゴリー (Luxury/Classic/Sports)\n\n### 期待される分析結果\n- どの金融属性の顧客がどの購買パターンを持つか\n- 来店頻度と購買額の相関\n- 商品嗜好と顧客属性の関係\n- 潜在的な高価値顧客の発見" + }, + { + "cell_type": "markdown", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": "## クロス集計表アルゴリズムの概要\n\nクロス集計表とは、2つ以上のカテゴリカル変数（質的変数）の関係を表形式で表現する統計手法です。このチュートリアルでは、銀行と高級時計会社の2つのデータセットをIDで突合し、クロス集計表を作成します。\n\n\n\n**処理の流れ**：\n1. 両組織が同一の顧客IDを持つレコードを特定\n2. IDが一致するレコード同士を安全に結合\n3. 結合されたデータから各属性の組み合わせをカウント\n4. カウント結果をクロス集計表として出力\n\n---\n\n## 実行方法\n\nこのユースケースの実行手順は、[基本チュートリアル](./basic_apc_cli_tutorial.ipynb)と同じです。以下の点のみ変更してください：\n\n**必須の変更:**\n- **関数のパス**: `FUNCTION_SOURCE_PATH=../../functions/cross_table`\n- **入力データのパス**:\n - `INPUT_1_PATH=../../functions/cross_table/inputs/bank_input`（銀行データ）\n - `INPUT_2_PATH=../../functions/cross_table/inputs/luxury_watch_input`（高級時計会社データ）\n- **handler.pyで読み込むファイル名**: `functions/cross_table/function/handler.py`内の入力ファイル名を以下のように変更：\n ```python\n # 変更前\n df_cross_table = cross_table_data(\n f\"{INPUT_1_DIR}/input_1.csv\", f\"{INPUT_2_DIR}/input_2.csv\"\n )\n \n # 変更後\n df_cross_table = cross_table_data(\n f\"{INPUT_1_DIR}/data.csv\", f\"{INPUT_2_DIR}/data.csv\"\n )\n ```\n\n**任意の変更（わかりやすくするため変更を推奨）:**\n- **プロファイル名**: 例として `bank-profile`（銀行用）と `luxury-watch-profile`（高級時計会社用）\n- **アプリケーション名**: 例として `cross_table_analysis`\n\n基本チュートリアルのステップ3〜15に従って、環境変数の設定からクリーンアップまでを実行してください。" + }, + { + "cell_type": "markdown", + "source": "---\n\n## 突合結果の例\n\n基本チュートリアルの手順に従って実行すると、以下のようなクロス集計結果が得られます：\n\n```\nnumber_of_rows,0:account_balance,0:credit_score,0:monthly_income,1:last_purchase_date,1:product_category_interest,1:store_visits_12months,1:total_purchase_amount\n11,Low,High,High,Over_3years,Sports,High,Low\n10,Low,Low,Medium,1-3years,Sports,Low,Low\n12,High,Medium,High,Over_3years,Sports,High,High\n10,Low,Low,Medium,1-3years,Classic,Low,Low\n10,Medium,High,Medium,1-3years,Classic,High,Medium\n10,Low,Low,Low,Within_1year,Sports,High,Low\n12,Low,Medium,Medium,1-3years,Classic,High,Low\n11,Medium,Medium,High,Over_3years,Luxury,Low,Medium\n11,Low,High,High,Over_3years,Classic,High,Low\n13,Medium,Low,High,Over_3years,Sports,High,Medium\n10,Medium,High,Low,Within_1year,Luxury,Low,Medium\n10,Low,Medium,High,Over_3years,Classic,High,Low\n11,Low,High,Medium,1-3years,Sports,High,Low\n10,Medium,High,Medium,1-3years,Classic,Low,Medium\n10,Medium,Low,High,Over_3years,Classic,High,Medium\n10,Low,Medium,High,Over_3years,Luxury,Low,Low\n11,High,High,Low,Within_1year,Luxury,High,High\n13,High,High,Medium,1-3years,Luxury,High,High\n```\n\n**データの見方:**\n- `number_of_rows`: この属性の組み合わせを持つ顧客数\n- `0:` で始まるカラム: 銀行データ（口座残高、信用スコア、月収）\n- `1:` で始まるカラム: 高級時計会社データ（購買履歴、来店頻度、関心カテゴリー）\n\n例えば、1行目は「口座残高Low、信用スコアHigh、月収Highで、3年以上前に購入し、Sports志向で来店頻度がHighだが購買額はLowの顧客が11人いる」ことを示しています。", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "---\n\n## 分析結果の考察\n\nクロス集計分析から得られる代表的な知見：\n\n### 1. **来店は多いが購買額は控えめな層が存在**\n\n→ よく店舗を訪れるものの、購買に至るケースは控えめという傾向が見られる顧客セグメント。\n\n**アクション例**: \n- 試着体験やエントリーモデルの提案を充実させ、購買につなげる\n- 来店ポイントプログラムで関係性を強化\n\n### 2. **嗜好と購買行動に多様性がある**\n\n→ スポーティなモデルを好む顧客の中には、購買額が安定しない傾向の方もいる。嗜好と購買行動の差異を理解することで、プロモーションや商品ラインナップの工夫が有効。\n\n**アクション例**:\n- 嗜好に応じた価格帯別商品展開\n- スポーツモデル愛好者向けアクセサリー提案\n\n### 3. **金融資産や信用が購買に影響するケース**\n\n→ 収入水準に関わらず、資産や信用力がある顧客は高額購買につながるケースが確認できる。所得だけでなく、資産・信用の観点から顧客を理解することが重要。\n\n**アクション例**:\n- 金融資産・信用力を基にしたVIPプログラム\n- 限定招待イベントでの接点作り\n\n### 4. **Luxury志向は幅広い層に分布**\n\n→ 高収入層だけでなく、中間層やその他層にも高級志向のニーズが見られる。高級志向は必ずしも所得や資産と連動せず、幅広い顧客に潜在ニーズが存在する。\n\n**アクション例**:\n- 「手の届くラグジュアリー」商品展開\n- 将来的な上位モデルへのステップアッププログラム", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "---\n\n## 他の応用ユースケース\n\nこのクロス集計分析フレームワークは、様々なドメインの組織間データ突合に応用可能です：\n\n### 1. **銀行 × 小売・EC（消費行動突合）**\n\n- **目的**: 購買力・嗜好・来店頻度を把握して、パーソナライズマーケティング\n- **例**:\n - 銀行口座残高・クレジット利用履歴 × スーパー／コンビニの購買履歴\n - クレジットカード支払い履歴 × オンラインファッション通販の購入履歴\n- **得られる知見**: 「高収入だけど頻度は少ない」「低収入でも嗜好が偏っている」など顧客セグメントの可視化\n\n### 2. **フィットネスジム × 健康管理アプリ**\n\n- **目的**: 顧客の健康行動と利用状況の連携\n- **例**:\n - ジム来店頻度 × 歩数や心拍データ\n - クラス予約履歴 × 栄養管理アプリの摂取カロリーデータ\n- **得られる知見**: 「ジムは来ているけど運動量は少ない層」「自己管理はしているがジムに来ない層」など\n\n### 3. **通販 × ソーシャルメディア嗜好**\n\n- **目的**: 広告ターゲティングや商品開発\n- **例**:\n - 購買履歴 × InstagramやTikTokのフォロー・いいね履歴\n - ECサイトの閲覧履歴 × YouTube動画視聴嗜好\n- **得られる知見**: 「ブランド好きだが購買にはつながっていない層」「購入傾向がSNSで予測できる層」\n\n### 4. **旅行会社 × 交通系カード**\n\n- **目的**: 顧客の旅行嗜好と消費能力の把握\n- **例**:\n - 航空券・ホテル予約履歴 × クレジットカード利用履歴\n - 移動距離（Suica/Pasmo等） × ホテル滞在履歴\n- **得られる知見**: 「旅行好きだけど予算が低い層」「高額旅行も余裕で楽しめる層」\n\n### 5. **飲食チェーン × 健康データ**\n\n- **目的**: ヘルシー志向・購買行動の把握\n- **例**:\n - 来店履歴・注文履歴 × ウェアラブルの食事・歩数データ\n - サブスクリプション利用履歴 × 体重・BMI推移\n- **得られる知見**: 「健康意識は高いが頻度は少ない層」「頻度は多いが偏った注文傾向の層」\n\n---\n\n**まとめ**\n\n異なるドメインを突合することで **隠れた顧客セグメントや意外な行動パターン**が見えてきます。AutoPrivacy DCRを活用することで、プライバシーを保護しながら、これまで見えなかった顧客インサイトを発見できます。", + "metadata": {} + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file From 9bc96f3953a207e63342040520fc53464fffd1e9 Mon Sep 17 00:00:00 2001 From: tak-ka3 Date: Thu, 9 Oct 2025 20:09:43 +0900 Subject: [PATCH 2/5] feat: add cross_table overview image --- notebooks/img/cross_table_overview.svg | 1 + 1 file changed, 1 insertion(+) create mode 100644 notebooks/img/cross_table_overview.svg diff --git a/notebooks/img/cross_table_overview.svg b/notebooks/img/cross_table_overview.svg new file mode 100644 index 0000000..ea29c3d --- /dev/null +++ b/notebooks/img/cross_table_overview.svg @@ -0,0 +1 @@ + \ No newline at end of file From 0cfc9e6f936c331e640a27467cb60ac3896352bd Mon Sep 17 00:00:00 2001 From: tak-ka3 Date: Thu, 16 Oct 2025 13:26:35 +0900 Subject: [PATCH 3/5] feat: add explanation of support by LLM --- ...dvanced_apc_cli_tutorial_cross_table.ipynb | 189 +++++++++++++++++- 1 file changed, 178 insertions(+), 11 deletions(-) diff --git a/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb b/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb index e0289f7..1537e56 100644 --- a/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb +++ b/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb @@ -3,38 +3,205 @@ { "cell_type": "markdown", "metadata": {}, - "source": "# 応用ユースケース：金融機関と高級時計会社のクロス集計分析\n\nこのチュートリアルでは、AutoPrivacy DCRを使用して、金融機関（銀行）と高級時計会社が保持するデータを安全に突合し、顧客の金融属性と購買行動を分析する応用例を紹介します。\n\n**重要**: このチュートリアルは、[基本チュートリアル](./basic_apc_cli_tutorial.ipynb)の実行手順を理解していることを前提としています。APC-CLIの具体的なコマンドや実行方法については基本チュートリアルを参照してください。\n\n---\n\n## ユースケースの背景\n\n企業が単独で保有するデータには限界があります：\n\n- **銀行** → 顧客の「収入・資産・信用度」などの金融的な側面は分かるが、**具体的にどんなモノにお金を使っているか**までは分からない\n- **高級時計会社** → 「どの顧客が時計を買ったか」「どのラインに興味があるか」は分かるが、その顧客の**収入水準や資産規模、信用力**は分からない\n\n## 解決策\n\n**AutoPrivacy DCR上でIDを元に安全に同意なしで突合することで「金融属性 × 購買実績」のクロス集計が可能になり、マーケティングROIを高められる**\n\n異なる企業のデータを組み合わせることで、単独では見えなかった顧客の全体像が明らかになります。" + "source": [ + "# 応用ユースケース：金融機関と高級時計会社のクロス集計分析\n", + "\n", + "このチュートリアルでは、AutoPrivacy DCRを使用して、金融機関（銀行）と高級時計会社が保持するデータを安全に突合し、顧客の金融属性と購買行動を分析する応用例を紹介します。\n", + "\n", + "**重要**: このチュートリアルは、[基本チュートリアル](./basic_apc_cli_tutorial.ipynb)の実行手順を理解していることを前提としています。APC-CLIの具体的なコマンドや実行方法については基本チュートリアルを参照してください。\n", + "\n", + "---\n", + "\n", + "## ユースケースの背景\n", + "\n", + "企業が単独で保有するデータには限界があります：\n", + "\n", + "- **銀行** → 顧客の「収入・資産・信用度」などの金融的な側面は分かるが、**具体的にどんなモノにお金を使っているか**までは分からない\n", + "- **高級時計会社** → 「どの顧客が時計を買ったか」「どのラインに興味があるか」は分かるが、その顧客の**収入水準や資産規模、信用力**は分からない\n", + "\n", + "## 解決策\n", + "\n", + "**AutoPrivacy DCR上でIDを元に安全に同意なしで突合することで「金融属性 × 購買実績」のクロス集計が可能になり、マーケティングROIを高められる**\n", + "\n", + "異なる企業のデータを組み合わせることで、単独では見えなかった顧客の全体像が明らかになります。" + ] }, { "cell_type": "markdown", "metadata": {}, - "source": "## データ概要\n\n### 銀行データ（入力1）\n- **account_balance**: 口座残高レベル (High/Medium/Low)\n- **monthly_income**: 月収レベル (High/Medium/Low) \n- **credit_score**: 信用スコア (High/Medium/Low)\n\n### 高級時計会社データ（入力2）\n- **total_purchase_amount**: 累計購入額 (High/Medium/Low)\n- **last_purchase_date**: 最終購入日からの経過 (Within_1year/1-3years/Over_3years)\n- **store_visits_12months**: 過去12か月の来店回数 (High/Medium/Low)\n- **product_category_interest**: 関心商品カテゴリー (Luxury/Classic/Sports)\n\n### 期待される分析結果\n- どの金融属性の顧客がどの購買パターンを持つか\n- 来店頻度と購買額の相関\n- 商品嗜好と顧客属性の関係\n- 潜在的な高価値顧客の発見" + "source": [ + "## データ概要\n", + "\n", + "### 銀行データ（入力1）\n", + "- **account_balance**: 口座残高レベル (High/Medium/Low)\n", + "- **monthly_income**: 月収レベル (High/Medium/Low) \n", + "- **credit_score**: 信用スコア (High/Medium/Low)\n", + "\n", + "### 高級時計会社データ（入力2）\n", + "- **total_purchase_amount**: 累計購入額 (High/Medium/Low)\n", + "- **last_purchase_date**: 最終購入日からの経過 (Within_1year/1-3years/Over_3years)\n", + "- **store_visits_12months**: 過去12か月の来店回数 (High/Medium/Low)\n", + "- **product_category_interest**: 関心商品カテゴリー (Luxury/Classic/Sports)\n", + "\n", + "### 期待される分析結果\n", + "- どの金融属性の顧客がどの購買パターンを持つか\n", + "- 来店頻度と購買額の相関\n", + "- 商品嗜好と顧客属性の関係\n", + "- 潜在的な高価値顧客の発見" + ] }, { "cell_type": "markdown", - "execution_count": null, "metadata": { "vscode": { "languageId": "plaintext" } }, - "outputs": [], - "source": "## クロス集計表アルゴリズムの概要\n\nクロス集計表とは、2つ以上のカテゴリカル変数（質的変数）の関係を表形式で表現する統計手法です。このチュートリアルでは、銀行と高級時計会社の2つのデータセットをIDで突合し、クロス集計表を作成します。\n\n\n\n**処理の流れ**：\n1. 両組織が同一の顧客IDを持つレコードを特定\n2. IDが一致するレコード同士を安全に結合\n3. 結合されたデータから各属性の組み合わせをカウント\n4. カウント結果をクロス集計表として出力\n\n---\n\n## 実行方法\n\nこのユースケースの実行手順は、[基本チュートリアル](./basic_apc_cli_tutorial.ipynb)と同じです。以下の点のみ変更してください：\n\n**必須の変更:**\n- **関数のパス**: `FUNCTION_SOURCE_PATH=../../functions/cross_table`\n- **入力データのパス**:\n - `INPUT_1_PATH=../../functions/cross_table/inputs/bank_input`（銀行データ）\n - `INPUT_2_PATH=../../functions/cross_table/inputs/luxury_watch_input`（高級時計会社データ）\n- **handler.pyで読み込むファイル名**: `functions/cross_table/function/handler.py`内の入力ファイル名を以下のように変更：\n ```python\n # 変更前\n df_cross_table = cross_table_data(\n f\"{INPUT_1_DIR}/input_1.csv\", f\"{INPUT_2_DIR}/input_2.csv\"\n )\n \n # 変更後\n df_cross_table = cross_table_data(\n f\"{INPUT_1_DIR}/data.csv\", f\"{INPUT_2_DIR}/data.csv\"\n )\n ```\n\n**任意の変更（わかりやすくするため変更を推奨）:**\n- **プロファイル名**: 例として `bank-profile`（銀行用）と `luxury-watch-profile`（高級時計会社用）\n- **アプリケーション名**: 例として `cross_table_analysis`\n\n基本チュートリアルのステップ3〜15に従って、環境変数の設定からクリーンアップまでを実行してください。" + "source": [ + "## クロス集計表アルゴリズムの概要\n", + "\n", + "クロス集計表とは、2つ以上のカテゴリカル変数（質的変数）の関係を表形式で表現する統計手法です。このチュートリアルでは、銀行と高級時計会社の2つのデータセットをIDで突合し、クロス集計表を作成します。\n", + "\n", + "\n", + "\n", + "**処理の流れ**：\n", + "1. 両組織が同一の顧客IDを持つレコードを特定\n", + "2. IDが一致するレコード同士を安全に結合\n", + "3. 結合されたデータから各属性の組み合わせをカウント\n", + "4. カウント結果をクロス集計表として出力\n", + "\n", + "---\n", + "\n", + "## 実行方法\n", + "\n", + "このユースケースの実行手順は、[基本チュートリアル](./basic_apc_cli_tutorial.ipynb)と同じです。以下の点のみ変更してください：\n", + "\n", + "**必須の変更:**\n", + "- **関数のパス**: `FUNCTION_SOURCE_PATH=../../functions/cross_table`\n", + "- **入力データのパス**:\n", + " - `INPUT_1_PATH=../../functions/cross_table/inputs/bank_input`（銀行データ）\n", + " - `INPUT_2_PATH=../../functions/cross_table/inputs/luxury_watch_input`（高級時計会社データ）\n", + "- **handler.pyで読み込むファイル名**: `functions/cross_table/function/handler.py`内の入力ファイル名を以下のように変更：\n", + " ```python\n", + " # 変更前\n", + " df_cross_table = cross_table_data(\n", + " f\"{INPUT_1_DIR}/input_1.csv\", f\"{INPUT_2_DIR}/input_2.csv\"\n", + " )\n", + " \n", + " # 変更後\n", + " df_cross_table = cross_table_data(\n", + " f\"{INPUT_1_DIR}/data.csv\", f\"{INPUT_2_DIR}/data.csv\"\n", + " )\n", + " ```\n", + "\n", + "**任意の変更（わかりやすくするため変更を推奨）:**\n", + "- **プロファイル名**: 例として `bank-profile`（銀行用）と `luxury-watch-profile`（高級時計会社用）\n", + "- **アプリケーション名**: 例として `cross_table_analysis`\n", + "\n", + "基本チュートリアルのステップ3〜15に従って、環境変数の設定からクリーンアップまでを実行してください。" + ] }, { "cell_type": "markdown", - "source": "---\n\n## 突合結果の例\n\n基本チュートリアルの手順に従って実行すると、以下のようなクロス集計結果が得られます：\n\n```\nnumber_of_rows,0:account_balance,0:credit_score,0:monthly_income,1:last_purchase_date,1:product_category_interest,1:store_visits_12months,1:total_purchase_amount\n11,Low,High,High,Over_3years,Sports,High,Low\n10,Low,Low,Medium,1-3years,Sports,Low,Low\n12,High,Medium,High,Over_3years,Sports,High,High\n10,Low,Low,Medium,1-3years,Classic,Low,Low\n10,Medium,High,Medium,1-3years,Classic,High,Medium\n10,Low,Low,Low,Within_1year,Sports,High,Low\n12,Low,Medium,Medium,1-3years,Classic,High,Low\n11,Medium,Medium,High,Over_3years,Luxury,Low,Medium\n11,Low,High,High,Over_3years,Classic,High,Low\n13,Medium,Low,High,Over_3years,Sports,High,Medium\n10,Medium,High,Low,Within_1year,Luxury,Low,Medium\n10,Low,Medium,High,Over_3years,Classic,High,Low\n11,Low,High,Medium,1-3years,Sports,High,Low\n10,Medium,High,Medium,1-3years,Classic,Low,Medium\n10,Medium,Low,High,Over_3years,Classic,High,Medium\n10,Low,Medium,High,Over_3years,Luxury,Low,Low\n11,High,High,Low,Within_1year,Luxury,High,High\n13,High,High,Medium,1-3years,Luxury,High,High\n```\n\n**データの見方:**\n- `number_of_rows`: この属性の組み合わせを持つ顧客数\n- `0:` で始まるカラム: 銀行データ（口座残高、信用スコア、月収）\n- `1:` で始まるカラム: 高級時計会社データ（購買履歴、来店頻度、関心カテゴリー）\n\n例えば、1行目は「口座残高Low、信用スコアHigh、月収Highで、3年以上前に購入し、Sports志向で来店頻度がHighだが購買額はLowの顧客が11人いる」ことを示しています。", - "metadata": {} + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 突合結果の例\n", + "\n", + "基本チュートリアルの手順に従って実行すると、以下のようなクロス集計結果が得られます：\n", + "\n", + "```\n", + "number_of_rows,0:account_balance,0:credit_score,0:monthly_income,1:last_purchase_date,1:product_category_interest,1:store_visits_12months,1:total_purchase_amount\n", + "11,Low,High,High,Over_3years,Sports,High,Low\n", + "10,Low,Low,Medium,1-3years,Sports,Low,Low\n", + "12,High,Medium,High,Over_3years,Sports,High,High\n", + "10,Low,Low,Medium,1-3years,Classic,Low,Low\n", + "10,Medium,High,Medium,1-3years,Classic,High,Medium\n", + "10,Low,Low,Low,Within_1year,Sports,High,Low\n", + "12,Low,Medium,Medium,1-3years,Classic,High,Low\n", + "11,Medium,Medium,High,Over_3years,Luxury,Low,Medium\n", + "11,Low,High,High,Over_3years,Classic,High,Low\n", + "13,Medium,Low,High,Over_3years,Sports,High,Medium\n", + "10,Medium,High,Low,Within_1year,Luxury,Low,Medium\n", + "10,Low,Medium,High,Over_3years,Classic,High,Low\n", + "11,Low,High,Medium,1-3years,Sports,High,Low\n", + "10,Medium,High,Medium,1-3years,Classic,Low,Medium\n", + "10,Medium,Low,High,Over_3years,Classic,High,Medium\n", + "10,Low,Medium,High,Over_3years,Luxury,Low,Low\n", + "11,High,High,Low,Within_1year,Luxury,High,High\n", + "13,High,High,Medium,1-3years,Luxury,High,High\n", + "```\n", + "\n", + "**データの見方:**\n", + "- `number_of_rows`: この属性の組み合わせを持つ顧客数\n", + "- `0:` で始まるカラム: 銀行データ（口座残高、信用スコア、月収）\n", + "- `1:` で始まるカラム: 高級時計会社データ（購買履歴、来店頻度、関心カテゴリー）\n", + "\n", + "例えば、1行目は「口座残高Low、信用スコアHigh、月収Highで、3年以上前に購入し、Sports志向で来店頻度がHighだが購買額はLowの顧客が11人いる」ことを示しています。" + ] }, { "cell_type": "markdown", - "source": "---\n\n## 分析結果の考察\n\nクロス集計分析から得られる代表的な知見：\n\n### 1. **来店は多いが購買額は控えめな層が存在**\n\n→ よく店舗を訪れるものの、購買に至るケースは控えめという傾向が見られる顧客セグメント。\n\n**アクション例**: \n- 試着体験やエントリーモデルの提案を充実させ、購買につなげる\n- 来店ポイントプログラムで関係性を強化\n\n### 2. **嗜好と購買行動に多様性がある**\n\n→ スポーティなモデルを好む顧客の中には、購買額が安定しない傾向の方もいる。嗜好と購買行動の差異を理解することで、プロモーションや商品ラインナップの工夫が有効。\n\n**アクション例**:\n- 嗜好に応じた価格帯別商品展開\n- スポーツモデル愛好者向けアクセサリー提案\n\n### 3. **金融資産や信用が購買に影響するケース**\n\n→ 収入水準に関わらず、資産や信用力がある顧客は高額購買につながるケースが確認できる。所得だけでなく、資産・信用の観点から顧客を理解することが重要。\n\n**アクション例**:\n- 金融資産・信用力を基にしたVIPプログラム\n- 限定招待イベントでの接点作り\n\n### 4. **Luxury志向は幅広い層に分布**\n\n→ 高収入層だけでなく、中間層やその他層にも高級志向のニーズが見られる。高級志向は必ずしも所得や資産と連動せず、幅広い顧客に潜在ニーズが存在する。\n\n**アクション例**:\n- 「手の届くラグジュアリー」商品展開\n- 将来的な上位モデルへのステップアッププログラム", - "metadata": {} + "metadata": {}, + "source": "---\n\n## 分析結果の考察\n\n上記のクロス集計表から、顧客の金融属性と購買行動の関係性を読み取ることができます。\n\n**分析のヒント**: 最近では、このようなクロス集計表をLLM（大規模言語モデル）に入力することで、簡単に傾向分析や顧客セグメントの可視化を行うことができます。例えば、CSV形式の結果をChatGPTやClaudeに貼り付けて「この顧客データから得られる知見をまとめてください」と尋ねるだけで、以下のような分析結果が得られます。\n\n### クロス集計分析から得られる代表的な知見：\n\n### 1. **来店は多いが購買額は控えめな層が存在**\n\n→ よく店舗を訪れるものの、購買に至るケースは控えめという傾向が見られる顧客セグメント。\n\n**アクション例**: \n- 試着体験やエントリーモデルの提案を充実させ、購買につなげる\n- 来店ポイントプログラムで関係性を強化\n\n### 2. **嗜好と購買行動に多様性がある**\n\n→ スポーティなモデルを好む顧客の中には、購買額が安定しない傾向の方もいる。嗜好と購買行動の差異を理解することで、プロモーションや商品ラインナップの工夫が有効。\n\n**アクション例**:\n- 嗜好に応じた価格帯別商品展開\n- スポーツモデル愛好者向けアクセサリー提案\n\n### 3. **金融資産や信用が購買に影響するケース**\n\n→ 収入水準に関わらず、資産や信用力がある顧客は高額購買につながるケースが確認できる。所得だけでなく、資産・信用の観点から顧客を理解することが重要。\n\n**アクション例**:\n- 金融資産・信用力を基にしたVIPプログラム\n- 限定招待イベントでの接点作り\n\n### 4. **Luxury志向は幅広い層に分布**\n\n→ 高収入層だけでなく、中間層やその他層にも高級志向のニーズが見られる。高級志向は必ずしも所得や資産と連動せず、幅広い顧客に潜在ニーズが存在する。\n\n**アクション例**:\n- 「手の届くラグジュアリー」商品展開\n- 将来的な上位モデルへのステップアッププログラム" }, { "cell_type": "markdown", - "source": "---\n\n## 他の応用ユースケース\n\nこのクロス集計分析フレームワークは、様々なドメインの組織間データ突合に応用可能です：\n\n### 1. **銀行 × 小売・EC（消費行動突合）**\n\n- **目的**: 購買力・嗜好・来店頻度を把握して、パーソナライズマーケティング\n- **例**:\n - 銀行口座残高・クレジット利用履歴 × スーパー／コンビニの購買履歴\n - クレジットカード支払い履歴 × オンラインファッション通販の購入履歴\n- **得られる知見**: 「高収入だけど頻度は少ない」「低収入でも嗜好が偏っている」など顧客セグメントの可視化\n\n### 2. **フィットネスジム × 健康管理アプリ**\n\n- **目的**: 顧客の健康行動と利用状況の連携\n- **例**:\n - ジム来店頻度 × 歩数や心拍データ\n - クラス予約履歴 × 栄養管理アプリの摂取カロリーデータ\n- **得られる知見**: 「ジムは来ているけど運動量は少ない層」「自己管理はしているがジムに来ない層」など\n\n### 3. **通販 × ソーシャルメディア嗜好**\n\n- **目的**: 広告ターゲティングや商品開発\n- **例**:\n - 購買履歴 × InstagramやTikTokのフォロー・いいね履歴\n - ECサイトの閲覧履歴 × YouTube動画視聴嗜好\n- **得られる知見**: 「ブランド好きだが購買にはつながっていない層」「購入傾向がSNSで予測できる層」\n\n### 4. **旅行会社 × 交通系カード**\n\n- **目的**: 顧客の旅行嗜好と消費能力の把握\n- **例**:\n - 航空券・ホテル予約履歴 × クレジットカード利用履歴\n - 移動距離（Suica/Pasmo等） × ホテル滞在履歴\n- **得られる知見**: 「旅行好きだけど予算が低い層」「高額旅行も余裕で楽しめる層」\n\n### 5. **飲食チェーン × 健康データ**\n\n- **目的**: ヘルシー志向・購買行動の把握\n- **例**:\n - 来店履歴・注文履歴 × ウェアラブルの食事・歩数データ\n - サブスクリプション利用履歴 × 体重・BMI推移\n- **得られる知見**: 「健康意識は高いが頻度は少ない層」「頻度は多いが偏った注文傾向の層」\n\n---\n\n**まとめ**\n\n異なるドメインを突合することで **隠れた顧客セグメントや意外な行動パターン**が見えてきます。AutoPrivacy DCRを活用することで、プライバシーを保護しながら、これまで見えなかった顧客インサイトを発見できます。", - "metadata": {} + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 他の応用ユースケース\n", + "\n", + "このクロス集計分析フレームワークは、様々なドメインの組織間データ突合に応用可能です：\n", + "\n", + "### 1. **銀行 × 小売・EC（消費行動突合）**\n", + "\n", + "- **目的**: 購買力・嗜好・来店頻度を把握して、パーソナライズマーケティング\n", + "- **例**:\n", + " - 銀行口座残高・クレジット利用履歴 × スーパー／コンビニの購買履歴\n", + " - クレジットカード支払い履歴 × オンラインファッション通販の購入履歴\n", + "- **得られる知見**: 「高収入だけど頻度は少ない」「低収入でも嗜好が偏っている」など顧客セグメントの可視化\n", + "\n", + "### 2. **フィットネスジム × 健康管理アプリ**\n", + "\n", + "- **目的**: 顧客の健康行動と利用状況の連携\n", + "- **例**:\n", + " - ジム来店頻度 × 歩数や心拍データ\n", + " - クラス予約履歴 × 栄養管理アプリの摂取カロリーデータ\n", + "- **得られる知見**: 「ジムは来ているけど運動量は少ない層」「自己管理はしているがジムに来ない層」など\n", + "\n", + "### 3. **通販 × ソーシャルメディア嗜好**\n", + "\n", + "- **目的**: 広告ターゲティングや商品開発\n", + "- **例**:\n", + " - 購買履歴 × InstagramやTikTokのフォロー・いいね履歴\n", + " - ECサイトの閲覧履歴 × YouTube動画視聴嗜好\n", + "- **得られる知見**: 「ブランド好きだが購買にはつながっていない層」「購入傾向がSNSで予測できる層」\n", + "\n", + "### 4. **旅行会社 × 交通系カード**\n", + "\n", + "- **目的**: 顧客の旅行嗜好と消費能力の把握\n", + "- **例**:\n", + " - 航空券・ホテル予約履歴 × クレジットカード利用履歴\n", + " - 移動距離（Suica/Pasmo等） × ホテル滞在履歴\n", + "- **得られる知見**: 「旅行好きだけど予算が低い層」「高額旅行も余裕で楽しめる層」\n", + "\n", + "### 5. **飲食チェーン × 健康データ**\n", + "\n", + "- **目的**: ヘルシー志向・購買行動の把握\n", + "- **例**:\n", + " - 来店履歴・注文履歴 × ウェアラブルの食事・歩数データ\n", + " - サブスクリプション利用履歴 × 体重・BMI推移\n", + "- **得られる知見**: 「健康意識は高いが頻度は少ない層」「頻度は多いが偏った注文傾向の層」\n", + "\n", + "---\n", + "\n", + "**まとめ**\n", + "\n", + "異なるドメインを突合することで **隠れた顧客セグメントや意外な行動パターン**が見えてきます。AutoPrivacy DCRを活用することで、プライバシーを保護しながら、これまで見えなかった顧客インサイトを発見できます。" + ] } ], "metadata": { From 773520c16e2c3c52d777ecc07d4e5122216a72d6 Mon Sep 17 00:00:00 2001 From: tak-ka3 Date: Thu, 16 Oct 2025 13:34:53 +0900 Subject: [PATCH 4/5] fix: delete unnecessary sentence --- ...dvanced_apc_cli_tutorial_cross_table.ipynb | 44 ++++++++++++++++++- 1 file changed, 42 insertions(+), 2 deletions(-) diff --git a/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb b/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb index 1537e56..bf8ad6b 100644 --- a/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb +++ b/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb @@ -144,7 +144,47 @@ { "cell_type": "markdown", "metadata": {}, - "source": "---\n\n## 分析結果の考察\n\n上記のクロス集計表から、顧客の金融属性と購買行動の関係性を読み取ることができます。\n\n**分析のヒント**: 最近では、このようなクロス集計表をLLM（大規模言語モデル）に入力することで、簡単に傾向分析や顧客セグメントの可視化を行うことができます。例えば、CSV形式の結果をChatGPTやClaudeに貼り付けて「この顧客データから得られる知見をまとめてください」と尋ねるだけで、以下のような分析結果が得られます。\n\n### クロス集計分析から得られる代表的な知見：\n\n### 1. **来店は多いが購買額は控えめな層が存在**\n\n→ よく店舗を訪れるものの、購買に至るケースは控えめという傾向が見られる顧客セグメント。\n\n**アクション例**: \n- 試着体験やエントリーモデルの提案を充実させ、購買につなげる\n- 来店ポイントプログラムで関係性を強化\n\n### 2. **嗜好と購買行動に多様性がある**\n\n→ スポーティなモデルを好む顧客の中には、購買額が安定しない傾向の方もいる。嗜好と購買行動の差異を理解することで、プロモーションや商品ラインナップの工夫が有効。\n\n**アクション例**:\n- 嗜好に応じた価格帯別商品展開\n- スポーツモデル愛好者向けアクセサリー提案\n\n### 3. **金融資産や信用が購買に影響するケース**\n\n→ 収入水準に関わらず、資産や信用力がある顧客は高額購買につながるケースが確認できる。所得だけでなく、資産・信用の観点から顧客を理解することが重要。\n\n**アクション例**:\n- 金融資産・信用力を基にしたVIPプログラム\n- 限定招待イベントでの接点作り\n\n### 4. **Luxury志向は幅広い層に分布**\n\n→ 高収入層だけでなく、中間層やその他層にも高級志向のニーズが見られる。高級志向は必ずしも所得や資産と連動せず、幅広い顧客に潜在ニーズが存在する。\n\n**アクション例**:\n- 「手の届くラグジュアリー」商品展開\n- 将来的な上位モデルへのステップアッププログラム" + "source": [ + "---\n", + "\n", + "## 分析結果の考察\n", + "\n", + "上記のクロス集計表から、顧客の金融属性と購買行動の関係性を読み取ることができます。\n", + "\n", + "**分析のヒント**: 最近では、このようなクロス集計表をLLM（大規模言語モデル）に入力することで、簡単に傾向分析や顧客セグメントの可視化を行うことができます。例えば、CSV形式の結果をChatGPTやClaudeに貼り付けて「この顧客データから得られる知見をまとめてください」と尋ねるだけで、以下のような分析結果が得られます。\n", + "\n", + "### 1. **来店は多いが購買額は控えめな層が存在**\n", + "\n", + "→ よく店舗を訪れるものの、購買に至るケースは控えめという傾向が見られる顧客セグメント。\n", + "\n", + "**アクション例**: \n", + "- 試着体験やエントリーモデルの提案を充実させ、購買につなげる\n", + "- 来店ポイントプログラムで関係性を強化\n", + "\n", + "### 2. **嗜好と購買行動に多様性がある**\n", + "\n", + "→ スポーティなモデルを好む顧客の中には、購買額が安定しない傾向の方もいる。嗜好と購買行動の差異を理解することで、プロモーションや商品ラインナップの工夫が有効。\n", + "\n", + "**アクション例**:\n", + "- 嗜好に応じた価格帯別商品展開\n", + "- スポーツモデル愛好者向けアクセサリー提案\n", + "\n", + "### 3. **金融資産や信用が購買に影響するケース**\n", + "\n", + "→ 収入水準に関わらず、資産や信用力がある顧客は高額購買につながるケースが確認できる。所得だけでなく、資産・信用の観点から顧客を理解することが重要。\n", + "\n", + "**アクション例**:\n", + "- 金融資産・信用力を基にしたVIPプログラム\n", + "- 限定招待イベントでの接点作り\n", + "\n", + "### 4. **Luxury志向は幅広い層に分布**\n", + "\n", + "→ 高収入層だけでなく、中間層やその他層にも高級志向のニーズが見られる。高級志向は必ずしも所得や資産と連動せず、幅広い顧客に潜在ニーズが存在する。\n", + "\n", + "**アクション例**:\n", + "- 「手の届くラグジュアリー」商品展開\n", + "- 将来的な上位モデルへのステップアッププログラム" + ] }, { "cell_type": "markdown", @@ -211,4 +251,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} From 1b0462e63bbe9984ba6eba346eddaaa540da216f Mon Sep 17 00:00:00 2001 From: tak-ka3 Date: Mon, 20 Oct 2025 17:58:35 +0900 Subject: [PATCH 5/5] feat: update img and improve sentences --- notebooks/img/cross_table_overview.svg | 2 +- .../tutorial/advanced_apc_cli_tutorial_cross_table.ipynb | 4 +--- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/notebooks/img/cross_table_overview.svg b/notebooks/img/cross_table_overview.svg index ea29c3d..aeac7b4 100644 --- a/notebooks/img/cross_table_overview.svg +++ b/notebooks/img/cross_table_overview.svg @@ -1 +1 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb b/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb index bf8ad6b..403f063 100644 --- a/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb +++ b/notebooks/tutorial/advanced_apc_cli_tutorial_cross_table.ipynb @@ -21,7 +21,7 @@ "\n", "## 解決策\n", "\n", - "**AutoPrivacy DCR上でIDを元に安全に同意なしで突合することで「金融属性 × 購買実績」のクロス集計が可能になり、マーケティングROIを高められる**\n", + "**AutoPrivacy DCR上でIDを元に安全に突合することで「金融属性 × 購買実績」のクロス集計が可能になり、マーケティングROIを高められる**\n", "\n", "異なる企業のデータを組み合わせることで、単独では見えなかった顧客の全体像が明らかになります。" ] @@ -151,8 +151,6 @@ "\n", "上記のクロス集計表から、顧客の金融属性と購買行動の関係性を読み取ることができます。\n", "\n", - "**分析のヒント**: 最近では、このようなクロス集計表をLLM（大規模言語モデル）に入力することで、簡単に傾向分析や顧客セグメントの可視化を行うことができます。例えば、CSV形式の結果をChatGPTやClaudeに貼り付けて「この顧客データから得られる知見をまとめてください」と尋ねるだけで、以下のような分析結果が得られます。\n", - "\n", "### 1. **来店は多いが購買額は控えめな層が存在**\n", "\n", "→ よく店舗を訪れるものの、購買に至るケースは控えめという傾向が見られる顧客セグメント。\n",