Problem Statement
What you'll see here
The business problem this analysis is solving, and the questions it answers.
Business Context
FoodHub is a food aggregator connecting customers, restaurants, and delivery partners through a single smartphone app. The platform earns a margin on every confirmed delivery, so customer satisfaction, restaurant performance, and delivery efficiency all feed directly into profitability.
Business Objective
Analyze historical order data to understand restaurant demand, customer behavior, delivery performance, ratings, and opportunities to improve the customer experience and platform economics.
Key business questions
1
Which restaurants and cuisines are most in demand?
Answered in Univariate
2
What drives customer ratings?
Answered in Multivariate
3
How do preparation and delivery times affect satisfaction?
Answered in Operations
4
Are there differences between weekday and weekend orders?
Answered in Multivariate
5
Which areas offer revenue growth opportunities?
Answered in Revenue
6
What business recommendations should FoodHub implement?
Answered in AI Insights
Why this problem matters
This is not simply a food delivery dataset. It is an operational intelligence problem involving customer demand, restaurant performance, delivery efficiency, and platform profitability. Understanding the interactions between cost, time-to-deliver, and customer ratings is what turns raw transactions into actionable business strategy.
AI: Why this matters for FoodHub
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