This team focuses on dissecting business and product case studies from the perspective of strategy, product, operations, and users, with the current main case study being: Retail Analytics Platform.

Main goal: build a portfolio to increase employability.

Web goal: provide an interactive analytics platform that helps users understand sales performance and obtain revenue predictions to support data-driven business 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?"

Useful links during project development:

This repo provides a retail star-schema dataset (data/) for query and data-analysis practice — one fact table and 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:

For a small team, one person may take on multiple roles.

1. Go to the Review page (click the task to open its page).
2. Write down the task's shortcomings at the bottom of the page.
3. Move the status to Starting Over / Reopen (drag on the board, or change the status directly on the page).

1. Click "+ New page" in the Brainstorm section.
2. Open the task page, then fill in the task details (task name, person in charge, and job description details).

1. Overview
   • Hero: Retail Analytics Platform tagline + image + button leading to the Analytics page
   • Trend summary for total sales, total orders, total customers, and average order value (in a single row)
   • A larger sales trend chart, 1-year time range with monthly intervals
   • A brief key-insight box based on the chart (e.g., "this year's sales trend is Rp100 million, an increase of X%")
2. Analytics
   • Embedded Tableau in the center of the page (1-year sales trend chart), with a dropdown for analysis by store, by product, by customer segment, and revenue
   • More detailed business insights
3. Forecast
   • Actual vs. predicted chart, with a dropdown for prediction target (revenue, customer, product — revenue prioritized) and forecast time horizon
   • Accuracy metrics display: MAE, RMSE, MAPE
   • Prediction table for several upcoming periods (month – prediction – range)
   • Business insights from the forecast results
4. What-if
   • Control panel at the top to change the promo percentage (discount_promo in fact_penjualan), including affected region and segment
   • Chart comparing the user-modified promo scenario vs. baseline
   • Estimated revenue & order volume, along with the percentage comparison against baseline
   • Business scenario insights
5. About us
   • Team members, their job descriptions, and LinkedIn links

Stakeholders need revenue analysis for each store, product category, and customer segment to understand current business performance and obtain revenue projections as a basis for decision-making.

Help stakeholders understand revenue performance, identify business opportunities and problems, project future performance, and evaluate various business scenarios.

• Which product categories generate the largest revenue in each region?
• How has revenue grown month over month over the past 1 year?
• Are premium products sold more in the Corporate or Consumer segment?
• Which stores have a higher average transaction value compared to their region's average?
• What is the revenue projection for the upcoming period?
• How can changes in certain metrics affect revenue?

• Monitor revenue performance by time, region, store, product category, and customer segment
• Identify factors affecting revenue performance and areas experiencing decline
• Provide predictions for revenue projections to support business planning
• Evaluate the impact of various changes/scenarios on revenue through what-if simulations
• Generate insights that can be used to determine revenue growth strategies

Monitor → Analyze → Predict → Simulate → Decide

Stakeholders can understand the overall business condition, identify areas that need improvement and growth opportunities, and make more measured decisions based on data.

Each case study uses a combination of the following frameworks to keep results consistent:

• 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.

flowchart TD
    A[1. Define Scope & Objectives] --> B[2. Establish Team Structure]
    B --> C[3. Build Analysis Framework]
    C --> D[4. Select & Validate Topic]
    D --> E[5. Collect Data & Research]
    E --> F[6. Multi-Perspective Analysis]
    F --> G[7. Formulate Insights & Recommendations]
    G --> H[8. Document in Standard Format]
    H --> I[9. Team Review & Discussion]
    I --> J[10. Publish & Gather Feedback]

.
├── data/                          # Retail star-schema dataset
│   └── README.md
├── research/
│   └── {case-name}/                # Raw research per case study
├── templates/
│   └── case-study-template.md      # Standard documentation template
├── framework.md                    # Analysis framework documentation
├── Docker.md
├── Data-Integration-Docker.md
├── Supabase-Migration.md
└── README.md

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