Lesson 6 - Analysis and Research with Claude

**Phase 1: Gather Sources** - Find 3-5 articles, reports, or data sources on your topic - Copy key sections (not entire articles — focus on relevant parts) - Paste into Claude with context: 'I'm researching [topic] for [purpose]' **Phase 2: Summarize** Prompt: 'Summarize each of these sources in 3-5 bullet points, focusing on [specific aspect]' Claude gives you the key points from each source. **Phase 3: Synthesize** Prompt: 'Now synthesize these summaries. What are the common themes? Where do sources disagree? What's missing?' Claude connects the dots across sources. **Phase 4: Extract Insights** Prompt: 'Based on this research, what are the top 3 insights for someone building [your product]? What should I do next?' Claude gives you actionable takeaways. **Example: Researching AI Coding Tools Market** 1. Gather: Articles on GitHub Copilot, Cursor, Replit Agent, market size 2. Summarize: 'Summarize each article's key points about user adoption and pricing' 3. Synthesize: 'What do these sources tell us about the AI coding tools market in 2024?' 4. Insights: 'Based on this, what differentiation strategy should a new AI coding tool use?' Result: Actionable market insights in 15 minutes.

**Step 1: Identify Competitors** Prompt: 'I'm building [your product]. Who are my top 5 competitors?' Claude suggests based on its knowledge (may need verification). **Step 2: Compare Features** Create a comparison table: Prompt: 'Create a comparison table for [Competitor A, B, C, My Product]. Compare on: pricing, key features, target audience, strengths, weaknesses. Format as markdown table.' Claude builds the table (you fill in 'My Product' column). **Step 3: Find Gaps** Prompt: 'Based on this comparison, what gaps exist in the market? What are these competitors NOT doing that users might want?' Claude identifies opportunities. **Step 4: Positioning** Prompt: 'Given this competitive landscape, how should I position my product to stand out? Give me 3 positioning strategies with pros/cons.' Claude suggests differentiation angles. **Example Output:** | Feature | Competitor A | Competitor B | My Product | |---------|-------------|-------------|------------| | Price | $29/mo | Free + $49 Pro | $19/mo | | Key Feature | AI chat | Code completion | Full projects | | Target | Developers | Students | Non-coders | | Strength | Speed | Free tier | Ease of use | | Weakness | Expensive | Limited | New/unproven | **Gaps:** No tool focuses on non-coders specifically. Most assume coding knowledge. **Positioning:** 'The only AI coding tool designed for people who don't code.'

**For Survey/Feedback Data:** - 'Analyze this user feedback and group into themes: [paste feedback]' - 'What are the top 3 pain points mentioned in this survey data?' - 'Sentiment analysis: what percentage is positive, negative, neutral?' - 'Quote the most insightful user responses' **For Metrics/Numbers:** - 'Analyze this data and tell me what stands out: [paste CSV]' - 'What trends do you see in this time-series data?' - 'Identify any outliers or anomalies in this dataset' - 'Compare these two sets of numbers and explain the difference' **For Conversion/Funnel Data:** - 'I have this funnel data: [paste]. Where are people dropping off?' - 'Based on these conversion rates, what should I optimize first?' - 'Calculate the conversion rate improvement needed to hit [goal]' **For A/B Test Results:** - 'I ran an A/B test: [results]. Is this statistically significant?' - 'Which variant won and by how much?' - 'Should I ship Variant B or run the test longer?' **For Qualitative Data (interviews, support tickets):** - 'Extract common themes from these interview transcripts: [paste]' - 'What features are users requesting most in these support tickets?' - 'Group these complaints into categories and prioritize by frequency' **General Analysis Prompt:** ``` Analyze this data and tell me: 1. What are the key patterns or trends? 2. What's surprising or unexpected? 3. What's the most important insight? 4. What action should I take based on this? [paste data] ```

**Recipe 1: Feature Brainstorm** ``` I'm building [product description]. Generate 20 feature ideas that would make this product more valuable to [target users]. Focus on features that: - Solve a real problem - Are buildable by a non-technical founder - Don't require complex infrastructure After generating, rank the top 5 by: impact, ease, and uniqueness. ``` **Recipe 2: Name Brainstorm** ``` I need a name for [product description]. Generate 30 name ideas that are: - Memorable and easy to pronounce - Related to [theme/concept] - Available as .com domain (check approximations) - Not trademarked (to your knowledge) Group by style: playful, professional, descriptive. ``` **Recipe 3: Problem-Solution Brainstorm** ``` Problem: [describe the problem] Generate 15 creative solutions. Include: - 5 obvious/conventional approaches - 5 unconventional/'crazy' ideas - 5 that combine elements of the above Then evaluate: which solution is most feasible for someone with [constraints]? ``` **Recipe 4: Marketing Angle Brainstorm** ``` Product: [description] Target Audience: [who] Generate 10 different marketing angles/value propositions. Each should: - Highlight a different benefit - Use a distinct emotional appeal - Be suitable for [platform: Twitter, LinkedIn, etc.] Example format: '[Benefit] for [audience] who [pain point]' ``` **Recipe 5: 'Worst Ideas' Brainstorm** ``` Generate the 10 WORST feature ideas for [product] — ideas that would definitely fail or anger users. Then, for each bad idea, explain what makes it bad. Finally, invert each one into a good idea. ``` **Recipe 6: Constraint-Based Brainstorm** ``` Brainstorm: [goal] Constraints: - Must be achievable in [timeframe] - Budget: [amount] or less - No [specific resource] available - Must work with [existing system/tool] Generate 10 ideas that respect all constraints. ```

**Synthesis:** Combining information from multiple sources to form a cohesive understanding **Qualitative Data:** Non-numerical data (text, feedback, interviews) **Quantitative Data:** Numerical data (metrics, measurements, counts) **Competitive Analysis:** Researching and comparing your competitors' offerings **Market Research:** Understanding your target market, customers, and landscape **Divergent Thinking:** Generating many ideas without judgment (brainstorming) **Convergent Thinking:** Evaluating and narrowing down to best options (deciding) **Insight:** A non-obvious understanding derived from data or research