Business and product case studies that uncover actionable insights from multiple business perspectives to support strategic product decisions.
This team focuses on dissecting real (or hypothetical) business and product case studies from various perspectives — strategy, product, operations, finance, and users — to produce insights that can be directly applied to product decision-making.
The final output of each case study is ideally not just academic analysis, but actionable recommendations: "what should the product team do based on these findings?"
This repo provides a retail star-schema dataset (data/) for query
and data-analysis practice — one fact table + four dimension tables, complete with
verified answers (see data/README.md).
To load this dataset into a database and practice real queries, run PostgreSQL locally via Docker:
Docker.md — how to install & run PostgreSQL in Docker, up to connecting from the VS Code PostgreSQL extension.
Data-Integration-Docker.md — how to load
the CSVs in data/ into that database.
Supabase-Migration.md — how to migrate that data to Supabase so it can be accessed online / without Docker.
- Train cross-functional strategic thinking (business, product, data, UX).
- Build a repository of case studies that can serve as an internal team reference.
- Produce concrete recommendations that can be tested or implemented.
- Sharpen data storytelling and insight presentation skills for stakeholders.
- Determine the focus: is the case study general industry, startup-specific, or internal to your own company?
- Establish the target audience for the case study results (e.g., product team, prospective investors, personal portfolio, learning community).
- Write a brief team mission statement (you can use the description you've already written as a starting point).
- Case Lead — determines the topic, coordinates the timeline.
- Business Analyst — analyzes business model, revenue, market positioning.
- Product Analyst — analyzes features, UX, product-market fit.
- Data/Research Support — gathers supporting data, secondary research.
- Writer/Editor — compiles the final narrative so it's easy to read.
For a small team, one person may take on multiple roles.
Establish a standard framework so every case study stays consistent, for example a combination of:
- Business Model Canvas — to understand the business model as a whole.
- SWOT / Porter's Five Forces — for competitive and strategic analysis.
- Jobs to be Done (JTBD) — to understand user motivation.
- RICE / ICE Scoring — to prioritize recommendations.
- North Star Metric & AARRR (Pirate Metrics) — for a growth/product perspective.
Document this framework in a separate file (/framework.md) so all team members use the same standard.
- Create a list of candidate companies/products worth dissecting.
- Prioritize based on: availability of public data, industry relevance, level of complexity.
- Validate with the team: is this topic rich enough to be analyzed from many perspectives?
- Sources: annual reports, news articles, interviews (if possible), public data (app store reviews, social listening, etc.).
- Record all sources to maintain credibility and avoid unverified claims.
- Store raw research in the
/research/{case-name}/folder.
Each case study should be analyzed from at least 3 perspectives, for example:
- Business Perspective: revenue model, unit economics, market positioning.
- Product Perspective: key features, user flow, differentiation.
- User Perspective: pain points, motivation, adoption barriers.
- Competitive Perspective: positioning relative to competitors.
- Insights must be specific and data-backed, not general opinions.
- Each insight should ideally be followed by a clear action recommendation: "Because of X, the product team should do Y to achieve Z."
- Use a prioritization framework (RICE/ICE) to rank which recommendations would have the most impact.
Create a case study template, for example:
1. Executive Summary
2. Company/Product Background
3. Problem/Challenge Discussed
4. Analysis (per perspective)
5. Key Insights
6. Strategic Recommendations
7. References/Data Sources
Store this template in /templates/case-study-template.md.
- Hold an internal review session before publishing — check the validity of the arguments, not just the writing quality.
- Invite a "devil's advocate" perspective to test how well the recommendations hold up.
- Publish the case study (internal blog, Notion, Medium, or this repo).
- Ask for feedback from outside the team to test whether the insights are truly actionable.
- Update the case study if new relevant data emerges.
- Set an output target (e.g., 1 case study every 2 weeks).
- Hold regular retrospectives: what can be improved in the analysis process or team collaboration.