Key questions the dashboard needed to answer:

• Which brands, cities, and branches contributed the most revenue?
• How did revenue and transactions change over time?
• Which menu items were the strongest revenue drivers?
• Which outlets had high traffic but low spending per transaction?
• How did customer spending behavior differ across brands?
• Which payment methods and sales channels were most frequently used?
• When were the busiest operational hours and days?
• How did staff performance and POS cycle time affect operations?

Drive / CSV Initial Load
        ↓
raw_transactions_staging
        ↓
raw_transactions
        ↓
vw_clean
        ↓
mv_dashboard_detail
        ↓
Looker Studio Dashboard

Page 01

Business Performance Dashboard

Screenshot of the Business Performance Dashboard page.

This page serves as the main executive overview of the business. It summarizes overall performance across all brands, cities, branches, and transaction channels. The purpose of this page is to help stakeholders quickly understand the company’s current business condition through core KPIs and high-level visual comparisons.

• Main KPIs: Total Revenue, Total Transactions, Average Transaction Value, Basket Size, Average Daily Revenue, Total Unit Sold, and Average Selling Price.
• Business comparison: Revenue by city, revenue by brand, branch performance, and weekly revenue trend.
• Customer behavior: Payment method distribution, spending tier, basket tier, visit purpose, and sales channel contribution.
• Analytical purpose: To identify the strongest revenue contributors and monitor overall business health from one centralized page.

Page 02

Operational and Staff Performance

Screenshot of the Operational and Staff Performance page.

This page focuses on operational efficiency and staff-related performance. While the first page explains business results, this page helps users understand how operations perform behind those results, including traffic patterns, waiter productivity, table usage, and transaction processing time.

• Staff monitoring: Waiter leaderboard, active waiters, and revenue contribution by staff.
• Operational timing: Daily traffic heatmap, weekday vs weekend pattern, and peak transaction hours.
• Table usage: Most-used table sections and detailed table usage distribution.
• Efficiency metric: Average POS cycle time to evaluate how long transactions take from order to completion.
• Analytical purpose: To support staffing decisions, operational scheduling, and peak-hour preparation.

Page 03

Menu Performance Analysis

Screenshot of the Menu Performance Analysis page.

This page analyzes menu-level performance across brands and categories. It was designed to help the business understand which menu items generate the most revenue, which products sell frequently, which items underperform, and how menu performance differs by transaction type and sales channel.

• Menu KPIs: Total menu revenue, total transaction, average selling price, average unit per bill, total unit sold, and discount amount.
• Top menu analysis: Top menu by revenue and top menu by quantity sold.
• Underperforming menu: Menu items with lower sales performance that may need review, promotion, or bundling strategy.
• Menu matrix: Classification of menu items into performance groups such as Star Menu, Popular Menu, Premium Menu, and Low Performer.
• Analytical purpose: To support menu optimization, product prioritization, bundling strategy, and promotional planning.

Page 04

Page Performance Per Brand

Screenshot of the Warung Makan Gula Padi Performance page (Example 1 Page).

• Brand KPIs: Revenue, transactions, average transaction value, basket size, average daily revenue, and total items sold.
• City comparison: Performance comparison across Bandung, Garut, and Tasikmalaya.
• Menu and category analysis: Revenue contribution by menu category, top menu, and underperforming menu.
• Customer behavior: Spending tier, basket tier, payment method, visit purpose, and online food channel usage.
• Analytical purpose: To monitor the main revenue-driving brand and identify city-level performance differences.

Key Metrics Used

Dashboard Metric Logic

Several calculated metrics were prepared in Looker Studio using the dashboard-ready BigQuery source. These metrics were designed to keep KPI definitions consistent across dashboard pages.

Total Revenue
= SUM(nett_sales)

Total Transactions
= COUNT_DISTINCT(bill_number)

Average Transaction Value
= SUM(nett_sales) / COUNT_DISTINCT(bill_number)

Basket Size
= SUM(billable_qty) / COUNT_DISTINCT(bill_number)

Average Daily Revenue
= SUM(nett_sales) / COUNT_DISTINCT(sales_date)

Total Items Sold
= SUM(billable_qty)

Average Selling Price
= SUM(nett_sales) / SUM(billable_qty)

Discount Rate
= ABS(SUM(discount)) / (SUM(nett_sales) + ABS(SUM(discount)))

Average POS Cycle Time
= (SUM(pos_cycle_sec_weight) / COUNT_DISTINCT(bill_number)) / 60

Analytical Value

The dashboard was not only built to display KPIs, but also to help stakeholders understand performance drivers across revenue, operations, menu contribution, customer behavior, and brand-level growth opportunities.

Output and Impact

The final dashboard provided a centralized performance monitoring system that allowed business users to evaluate performance from multiple perspectives. It helped convert raw POS transaction records into structured insights for management, operations, and marketing decision-making.

Tools and Technologies

Key Learnings

This project strengthened my understanding of how to build a dashboard not only as a visualization tool, but also as a structured analytics product. The most important part was designing the data flow properly before building the dashboard interface.