How To Write Case Studies That Win Trust

Good case studies are not glossy ads. They are clear records. They show the problem, the choice, the result, and the proof. In an age where AI can draft code fast, trust comes from slow thinking and honest evidence.

Goal: give you a simple way to plan, write, and publish case studies that help clients and hiring teams believe you can deliver.

What makes a good case study#

• Outcome first. Start with one line that says what changed and for whom.
• Plain context. Who had the problem, what made it hard, and what was in scope.
• Clear goal. Set a clear goal, name the one result you will improve, and choose a measurable figure such as time in minutes, a percentage, or a count that shows it worked.
• Simple story. Before, actions taken, after. Fewer steps, clearer flow.
• Evidence. Screens, charts, or numbers with dates and a short note on how you measured.
• Trade offs. What you did not do and why. Honesty builds trust.
• Safety and care. Note privacy, security, and reliability choices.
• Lessons. What surprised you, what you would do next time, and what you will not do again.
• Receipts. Link to briefs, diagrams, dashboards, and runbooks.

The blueprint#

Use these sections in this order. Keep the language simple.

1) One line outcome#

Say what changed and for whom.

Cut quote time from five minutes to ninety seconds for small retailers.

2) Context#

Who had the problem. What constraint made it hard. What was in and out of scope.

3) Goal and measure#

Name one result to improve and how you will measure it. Include baseline, target, and time window.

4) Before snapshot#

Show what life looked like before. A short video or two screens are enough.

5) Approach#

Key choices you made. Keep it to three to five bullets. Include why this path made sense.

6) AI role#

Say if AI was used to generate, classify, retrieve, or rank. Note guard rails and how you checked quality.

7) Architecture and reliability#

A simple diagram and a note on rate limits, retries, and how the system handles failure.

8) Security and privacy#

What data you collected, how you protected it, and how the user can export or delete it.

9) Result and proof#

Show the new number. Add a chart or a table. State the date range and the sample size.

10) What did not work#

Name at least one idea you tried that you rolled back. Say why.

11) Cost and performance#

Note cloud cost before and after. Note P50 and P95 latency if it matters.

12) Next steps#

One or two improvements you would make next and why they matter.

13) Artifacts#

Links to the brief, diagrams, dashboards, tests, and the runbook.

A fill in template you can copy#

# <Project Name>: <Outcome in one line>

**Context**
Who had the problem. What constraint made it hard. What was in scope and out of scope.

**Goal and measure**
Baseline, target, result, and time window.

**Before**
Two screens or a short video that shows the pain.

**Approach**

- Choice 1 and why
- Choice 2 and why
- Choice 3 and why

**AI role**
Generate or classify or retrieve or rank. Guard rails and how you checked quality.

**Architecture and reliability**
One small diagram. Note retries, idempotency, rate limits, and how failure is handled.

**Security and privacy**
Data collected, how it is protected, and how users can export or delete it.

**Result and proof**
The new number with a chart. Date range and sample size. How you measured.

**What did not work**
One idea you removed and why.

**Cost and performance**
Cost before and after. P50 and P95 if relevant.

**Next steps**
Two moves you would make and why.

**Artifacts**
Links to brief, diagrams, dashboards, tests, and runbook.

How to find the numbers#

• Pick a time window that matches the work. For example two weeks before and two weeks after.
• Track one main number. For example sign up completion, refund rate, or time to first value.
• Add a short note on method. For example events from the app, sample of one thousand users, outliers removed.
• Use round numbers where it helps. For example forty two percent to nineteen percent.

Visuals that help#

• One small diagram with labels. Keep it simple.
• Two or three screens with callouts that show the change.
• A short ninety second video that walks the path.
• Alt text for each image to help more readers.

Privacy and consent#

• Remove personal info from screens and logs.
• Get permission before you share client work. If not, change names and blur data.
• Do not share secrets or keys. Do not include internal links.

Common mistakes to avoid#

• Starting with tech and not the problem.
• Five goals instead of one.
• Screens with no numbers.
• Buzzwords and no plain talk.
• No dates, no sample size, no source of truth.

A quick review checklist#

[ ] One line outcome is clear
[ ] One goal and one number are named with dates
[ ] Before and after are shown
[ ] Approach has three to five choices with reasons
[ ] AI role and checks are clear
[ ] Diagram shows the shape of the system
[ ] Security and privacy notes are present
[ ] Result has a chart with method notes
[ ] One thing that failed is named
[ ] Next steps are realistic
[ ] Artifacts are linked

A two day writing plan#

Day 1

• Draft context, goal, and before snapshot.
• List three choices and the AI role.
• Add diagram and privacy notes.

Day 2

• Pull numbers and make one chart.
• Write result, what failed, and next steps.
• Link artifacts. Read it out loud. Cut extra words.

Start here#

• Read Shaplink case study

Closing note#

A good case study helps a busy person make a decision. Keep it short, honest, and complete. Outcome first. Proof on the page. Respect for users. That is how you build trust and win work.