Case Study Writing

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

| 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

4. Task / Challenge

5. Action / Solution

6. Results

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

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 | | PDF | 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

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

Browse all apps: infsh app list

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.