case-study-writing
B2B case study writing with STAR framework, data visualization, and research. Covers structure, customer quotes, metrics presentation, and distribution formats. Use for: customer success stories, portfolio pieces, sales enablement, marketing content. Triggers: case study, customer story, success story, b2b case study, client testimonial, customer case study, portfolio case study, use case, customer win, results story
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- 2026-05-17
- 取得日時
- 2026-05-17
- 同梱ファイル
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[Skill 名] case-study-writing
ケーススタディの作成
inference.sh CLI を使用して、調査とビジュアルを盛り込んだ説得力のある B2B ケーススタディを作成します。
クイックスタート
curl -fsSL https://cli.inference.sh | sh && infsh login
# 顧客の業界を調査します
infsh app run tavily/search-assistant --input '{
"query": "SaaS customer onboarding challenges 2024 statistics"
}'
STAR フレームワーク
すべてのケーススタディは、Situation (状況) -> Task (課題) -> Action (行動) -> Result (結果) に従います。
| セクション | 長さ | 内容 | 目的 |
|---|---|---|---|
| Situation | 100-150 語 | 顧客は誰か、その背景 | 場面設定 |
| Task | 100-150 語 | 彼らが直面した具体的な課題 | 共感を呼ぶ |
| Action | 200-300 語 | どのようなソリューションが導入されたか、その方法 | 製品を紹介する |
| Result | 100-200 語 | 測定可能な成果、Before/After | 価値を証明する |
合計: 800-1200 語。 これより長いと読者を失います。これより短いと信頼性に欠けます。
構造テンプレート
1. 見出し (結果から始める)
❌ "How Company X Uses Our Product"
❌ "Company X Case Study"
✅ "How Company X Reduced Onboarding Time by 60% with [Product]"
✅ "Company X Grew Revenue 340% in 6 Months Using [Product]"
見出しは具体的で、定量化され、結果を述べる必要があります。
2. スナップショットボックス
流し読みする人のために上部に配置します。
┌─────────────────────────────────────┐
│ Company: Acme Corp │
│ Industry: E-commerce │
│ Size: 200 employees │
│ Challenge: Manual order processing │
│ Result: 60% faster fulfillment │
│ Product: [Your Product] │
└─────────────────────────────────────┘
3. Situation (状況)
- 顧客は誰か (業界、規模、所在地)
- 問題発生前に存在した関連する背景
- 会社の背景を 1-2 文で説明
4. Task / Challenge (課題)
- 痛みを定量化する: 「データの問題があった」ではなく、「手動データ入力に週 40 時間を費やしていた」
- リスクを示す: 未解決の場合に何が起こるか (収益損失、チャーン、納期遅延)
- 不満に関する顧客の引用を含める
5. Action / Solution (行動 / ソリューション)
- 何が導入されたか (あなたの製品/サービス)
- タイムライン: 「2 週間で展開」 / 「3 ヶ月のロールアウト」
- 主要な決定事項または構成
- なぜ代替案ではなくあなたを選んだのか (簡潔に)
- 課題に対処した 2-3 の具体的な機能
6. Results (結果)
- Before/After の指標 — 常に定量化する
- 期間 — 「3 ヶ月以内」 / 「最初の四半期に」
- 元々の目標を超えた予期せぬメリット
- 成果に関する顧客の引用
重要な指標
数値の提示方法
❌ "Improved efficiency"
❌ "Saved time"
❌ "Better results"
✅ "Reduced processing time from 4 hours to 45 minutes (81% decrease)"
✅ "Increased conversion rate from 2.1% to 5.8% (176% improvement)"
✅ "Saved $240,000 annually in operational costs"
指標のカテゴリ
| カテゴリ | 例 |
|---|---|
| 時間 | 削減された時間、完了までの時間、展開速度 |
| 費用 | 収益増加、コスト削減、ROI |
| 効率 | スループット、エラー率、自動化率 |
| 成長 | 獲得ユーザー数、市場拡大、機能採用 |
| 満足度 | NPS の変化、維持率、削減されたサポートチケット |
データビジュアライゼーション
# Before/After の比較チャートを生成します
infsh app run infsh/python-executor --input '{
"code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\ncategories = [\"Processing Time\", \"Error Rate\", \"Cost per Order\"]\nbefore = [4, 12, 8.50]\nafter = [0.75, 1.5, 2.10]\n\nfig, ax = plt.subplots(figsize=(10, 6))\nx = range(len(categories))\nwidth = 0.35\nax.bar([i - width/2 for i in x], before, width, label=\"Before\", color=\"#ef4444\")\nax.bar([i + width/2 for i in x], after, width, label=\"After\", color=\"#22c55e\")\nax.set_ylabel(\"Value\")\nax.set_xticks(x)\nax.set_xticklabels(categories)\nax.legend()\nax.set_title(\"Impact of Implementation\")\nplt.tight_layout()\nplt.savefig(\"results-chart.png\", dpi=150)\nprint(\"Chart saved\")"
}'
顧客の引用
良い引用とは
❌ "We love the product." (曖昧で、何についてでもあり得る)
❌ "It's great." (意味がない)
✅ "We went from processing 50 orders a day to 200, without adding a single person to the team."
— Sarah Chen, VP Operations, Acme Corp
✅ "Before [Product], our team dreaded Monday mornings because of the report backlog.
Now it's automated and they can focus on actual analysis."
— Marcus Rodriguez, Head of Analytics, DataCo
引用の配置
- 課題セクションに 1 つの引用 — 不満/痛みについて
- 結果セクションに 1-2 つの引用 — 成果/変革について
- 常に帰属を示す: フルネーム、役職、会社名
引用の書式設定
> "We went from processing 50 orders a day to 200, without adding anyone to the team."
>
> — Sarah Chen, VP Operations, Acme Corp
調査サポート
業界の背景を見つける
# 業界ベンチマーク
infsh app run tavily/search-assistant --input '{
"query": "average e-commerce order processing time industry benchmark 2024"
}'
# 競合他社の状況
infsh app run exa/search --input '{
"query": "order management automation solutions market overview"
}'
# 裏付けとなる統計
infsh app run exa/answer --input '{
"question": "What percentage of e-commerce businesses still use manual order processing?"
}'
配布形式
| 形式 | 場所 | 備考 |
|---|---|---|
| Web ページ | /customers/ または /case-studies/ | 完全版、SEO 最適化済み |
| 営業チーム、メール添付 | デザイン済み、ダウンロード可能、ゲート付きオプション | |
| スライドデッキ | 営業電話、プレゼンテーション | 5-8 スライド、ビジュアル重視 |
| ワンペーパー | 展示会、クイックリファレンス | スナップショット + 主要指標 + 引用 |
| ソーシャル投稿 | LinkedIn, Twitter | 主要な統計 + 引用 + 完全版へのリンク |
| 動画 | ウェブサイト、YouTube | 顧客インタビューまたはアニメーション |
ソーシャルメディアスニペット
📜 原文 SKILL.md(Claudeが読む英語/中国語)を展開
Case Study Writing
Create compelling B2B case studies with research and visuals via inference.sh CLI.
Quick Start
curl -fsSL https://cli.inference.sh | sh && infsh login
# Research the customer's industry
infsh app run tavily/search-assistant --input '{
"query": "SaaS customer onboarding challenges 2024 statistics"
}'
The STAR Framework
Every case study follows: Situation -> Task -> Action -> Result
| Section | Length | Content | Purpose |
|---|---|---|---|
| Situation | 100-150 words | Who the customer is, their context | Set the scene |
| Task | 100-150 words | The specific challenge they faced | Create empathy |
| Action | 200-300 words | What solution was implemented, how | Show your product |
| Result | 100-200 words | Measurable outcomes, before/after | Prove value |
Total: 800-1200 words. Longer loses readers. Shorter lacks credibility.
Structure Template
1. Headline (Lead with the Result)
❌ "How Company X Uses Our Product"
❌ "Company X Case Study"
✅ "How Company X Reduced Onboarding Time by 60% with [Product]"
✅ "Company X Grew Revenue 340% in 6 Months Using [Product]"
The headline should be specific, quantified, and state the outcome.
2. Snapshot Box
Place at the top for skimmers:
┌─────────────────────────────────────┐
│ Company: Acme Corp │
│ Industry: E-commerce │
│ Size: 200 employees │
│ Challenge: Manual order processing │
│ Result: 60% faster fulfillment │
│ Product: [Your Product] │
└─────────────────────────────────────┘
3. Situation
- Who is the customer (industry, size, location)
- What relevant context existed before the problem
- 1-2 sentences of company background
4. Task / Challenge
- Quantify the pain: "spending 40 hours/week on manual data entry" not "had data problems"
- Show stakes: what would happen if unsolved (lost revenue, churn, missed deadlines)
- Include a customer quote about the frustration
5. Action / Solution
- What was implemented (your product/service)
- Timeline: "deployed in 2 weeks" / "3-month rollout"
- Key decisions or configurations
- Why they chose you over alternatives (briefly)
- 2-3 specific features that addressed the challenge
6. Results
- Before/after metrics — always quantified
- Timeframe — "within 3 months" / "in the first quarter"
- Unexpected benefits beyond the original goal
- Customer quote about the outcome
Metrics That Matter
How to Present Numbers
❌ "Improved efficiency"
❌ "Saved time"
❌ "Better results"
✅ "Reduced processing time from 4 hours to 45 minutes (81% decrease)"
✅ "Increased conversion rate from 2.1% to 5.8% (176% improvement)"
✅ "Saved $240,000 annually in operational costs"
Metric Categories
| Category | Examples |
|---|---|
| Time | Hours saved, time-to-completion, deployment speed |
| Money | Revenue increase, cost reduction, ROI |
| Efficiency | Throughput, error rate, automation rate |
| Growth | Users gained, market expansion, feature adoption |
| Satisfaction | NPS change, retention rate, support tickets reduced |
Data Visualization
# Generate a before/after comparison chart
infsh app run infsh/python-executor --input '{
"code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\ncategories = [\"Processing Time\", \"Error Rate\", \"Cost per Order\"]\nbefore = [4, 12, 8.50]\nafter = [0.75, 1.5, 2.10]\n\nfig, ax = plt.subplots(figsize=(10, 6))\nx = range(len(categories))\nwidth = 0.35\nax.bar([i - width/2 for i in x], before, width, label=\"Before\", color=\"#ef4444\")\nax.bar([i + width/2 for i in x], after, width, label=\"After\", color=\"#22c55e\")\nax.set_ylabel(\"Value\")\nax.set_xticks(x)\nax.set_xticklabels(categories)\nax.legend()\nax.set_title(\"Impact of Implementation\")\nplt.tight_layout()\nplt.savefig(\"results-chart.png\", dpi=150)\nprint(\"Chart saved\")"
}'
Customer Quotes
What Makes a Good Quote
❌ "We love the product." (vague, could be about anything)
❌ "It's great." (meaningless)
✅ "We went from processing 50 orders a day to 200, without adding a single person to the team."
— Sarah Chen, VP Operations, Acme Corp
✅ "Before [Product], our team dreaded Monday mornings because of the report backlog.
Now it's automated and they can focus on actual analysis."
— Marcus Rodriguez, Head of Analytics, DataCo
Quote Placement
- 1 quote in the Challenge section — about the frustration/pain
- 1-2 quotes in the Results section — about the outcome/transformation
- Always attribute: full name, title, company
Quote Formatting
> "We went from processing 50 orders a day to 200, without adding anyone to the team."
>
> — Sarah Chen, VP Operations, Acme Corp
Research Support
Finding Industry Context
# Industry benchmarks
infsh app run tavily/search-assistant --input '{
"query": "average e-commerce order processing time industry benchmark 2024"
}'
# Competitor landscape
infsh app run exa/search --input '{
"query": "order management automation solutions market overview"
}'
# Supporting statistics
infsh app run exa/answer --input '{
"question": "What percentage of e-commerce businesses still use manual order processing?"
}'
Distribution Formats
| Format | Where | Notes |
|---|---|---|
| Web page | /customers/ or /case-studies/ | Full version, SEO-optimized |
| Sales team, email attachment | Designed, downloadable, gated optional | |
| Slide deck | Sales calls, presentations | 5-8 slides, visual-heavy |
| One-pager | Trade shows, quick reference | Snapshot + key metrics + quote |
| Social post | LinkedIn, Twitter | Key stat + quote + link to full |
| Video | Website, YouTube | Customer interview or animated |
Social Media Snippet
Headline stat + brief context + customer quote + CTA
Example:
"60% faster order processing.
Acme Corp was drowning in manual fulfillment. 4 hours per batch. 12% error rate.
After implementing [Product]: 45 minutes per batch. 1.5% errors.
'We went from 50 orders a day to 200 without adding headcount.' — Sarah Chen, VP Ops
Read the full story → [link]"
Writing Checklist
- [ ] Headline leads with the quantified result
- [ ] Snapshot box with company, industry, challenge, result at top
- [ ] Challenge is quantified, not vague
- [ ] 2-3 specific customer quotes with attribution
- [ ] Before/after metrics with timeframe
- [ ] 800-1200 words total
- [ ] Skimmable (headers, bold, bullet points)
- [ ] Customer approved the final version
- [ ] Visual: at least one chart or before/after comparison
Common Mistakes
| Mistake | Problem | Fix |
|---|---|---|
| No specific numbers | Reads like marketing fluff | Quantify everything |
| All about your product | Reads like a sales pitch | Story is about the CUSTOMER |
| Generic quotes | No credibility | Get specific, attributed quotes |
| Missing the "before" | No contrast to show impact | Always show the starting point |
| Too long | Loses reader attention | 800-1200 words max |
| No customer approval | Legal/relationship risk | Always get sign-off |
Related Skills
npx skills add inferencesh/skills@web-search
npx skills add inferencesh/skills@prompt-engineering
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