case-study-writing（案例研究写作）

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"
}'

Install note: The install script only detects your OS/architecture, downloads the matching binary from dist.inference.sh, and verifies its SHA-256 checksum. No elevated permissions or background processes. Manual install & verification available.

The STAR Framework

Every case study follows: Situation -> Task -> Action -> Result

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

Data Visualization

# Generate a before/after comparison chart
infsh app run infsh/python-executor --input '{
  "code": "import matplotlib.pyplot as plt\
import matplotlib\
matplotlib.use(\"Agg\")\
\
categories = [\"Processing Time\", \"Error Rate\", \"Cost per Order\"]\
before = [4, 12, 8.50]\
after = [0.75, 1.5, 2.10]\
\
fig, ax = plt.subplots(figsize=(10, 6))\
x = range(len(categories))\
width = 0.35\
ax.bar([i - width/2 for i in x], before, width, label=\"Before\", color=\"#ef4444\")\
ax.bar([i + width/2 for i in x], after, width, label=\"After\", color=\"#22c55e\")\
ax.set_ylabel(\"Value\")\
ax.set_xticks(x)\
ax.set_xticklabels(categories)\
ax.legend()\
ax.set_title(\"Impact of Implementation\")\
plt.tight_layout()\
plt.savefig(\"results-chart.png\", dpi=150)\
print(\"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

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

Related Skills

npx skills add inference-sh/skills@web-search
npx skills add inference-sh/skills@prompt-engineering

Browse all apps: infsh app list