The Expedia analysis started as a raw dataset: 100,000 hotel search records with outcome signals (clicks, bookings, prices, ratings). The challenge wasn't the data—it was turning raw numbers into insights and understanding what the data was really saying.
This is where Claude Code made the difference. Instead of spending days writing boilerplate data cleaning code, I focused on asking better questions and letting Claude Code handle the mechanical work. Here's how.
The Workflow: From Data to Insights
Step 1: Load and explore. I imported the dataset into Claude Code and asked it to profile the data—shape, missing values, distributions. Within seconds, I had clarity on what I was working with.
Step 2: Hypothesis generation. Instead of running blind statistical tests, I used Claude Code to help structure hypotheses: "What if price isn't the conversion lever? What if position matters more?" Claude Code then wrote the queries to test each hypothesis.
Step 3: Segmented analysis. Breaking down booking rates by segment (Budget, Mid, Luxury) was straightforward in pandas, but Claude Code automated the comparison tables and cross-segment calculations, catching patterns I might have missed manually.
Step 4: Visualization. The deck you see was built with Claude Code writing Chart.js, HTML, and CSS. Instead of struggling with charting libraries, I described what I wanted to show—the trust gap, position elasticity, segment differences—and Claude Code structured it visually.
Step 5: Documentation. The case study writeup was generated with Claude Code helping structure the narrative, ensuring each finding was backed by data and clearly explained.
Where Claude Code Saved the Most Time
Data cleaning and transformation: Claude Code wrote efficient pandas pipelines without me having to debug data types or handle edge cases manually.
Exploratory analysis: Instead of writing multiple scripts to test different hypotheses, Claude Code generated them on-demand as I asked questions.
Visualization: Building interactive charts and presentations is usually time-consuming. Claude Code wrote the HTML/CSS/JS deck in a fraction of the time it would take manually.
Documentation and storytelling: Claude Code helped articulate findings clearly, ensuring the technical analysis was accessible to non-technical stakeholders.
Key Lessons
1. Ask better questions, not more questions. With Claude Code handling code generation, I could focus on asking more targeted questions about the data.
2. Outcome-level thinking. Claude Code doesn't care about convention. When I asked "which levers actually move conversion?", it tested outcomes directly instead of proxies.
3. Iteration is fast. Changing a hypothesis or exploring a new angle took minutes instead of hours. This enabled deeper analysis in less time.
4. Documentation is first-class. Because Claude Code generates narratives alongside code, the analysis is self-documenting. Anyone can understand the journey from data to recommendation.
The Real Win
The real win wasn't speed—it was depth. With less time spent on mechanical coding, I spent more time thinking deeply about marketplace dynamics, questioning assumptions, and stress-testing conclusions. The analysis is stronger because Claude Code handled the grunt work.
For Your Next Analysis
If you're building data-driven insights, start with Claude Code. Define your outcome metric, articulate your hypothesis, and let Claude Code do the mechanical work. You'll find yourself asking better questions faster, and your findings will be stronger.