Obtain a comprehensive view of restaurant performance across multiple dimensions, including price segments, customer ratings, and operational timings.
Identify key trends and insights such as popular dining times, top restaurant types, and highperforming chains and districts.
Enable granular, data-driven decisions to optimize pricing, menu offerings, and operational strategies.
Overcome challenges related to disparate data sources and the need for dynamic filtering to support localized insights.
Reduce operational costs associated with legacy infrastructure and licensing.
Solution/Approach
Developed an interactive Power BI dashboard integrating multi-source data from various cloud kitchen’s API data.
Built visualizations that analyze the number of restaurants by price, popular time slots, types of restaurants, and customer ratings.
Implemented dynamic filters for chain name, district, price group, and zone, allowing users to drill down into granular insights.
Provided charts highlighting top sold-out foods, top fast-food chains, and district-specific performance metrics to guide strategic decision-making.
Technologies
Power BI
API Fetching
Diagram/Images/Screenshots:
The case study includes screenshots showcasing key dashboard components: charts on restaurant counts by price, popular time trends, breakdowns of restaurant types, and
customer rating summaries.
Business Outcome/Benefits/Results:
Enhanced visibility into performance across various restaurant segments and geographic areas.
Empowered decision-makers to fine-tune marketing strategies, operational hours, and menu
offerings based on actionable insights.
Improved competitive positioning by benchmarking performance across chains and districts.
Supported data-driven initiatives that contributed to improved customer satisfaction and
overall operational efficiency.
