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Yearly Pizza Market Trends and Sales
Food & Beverage Consumption
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About
Pizza consumption drives a massive global industry, with millions of units sold daily. Understanding the dynamics of this market provides vital intelligence for business optimization and academic research. This dataset offers an extensive view of sales trends, capturing granular details on volume, revenue, and customer choices. It aggregates transactional records from a variety of pizza restaurants and chains, spanning diverse regions and operational scales to facilitate deep analysis of the sector.
Columns
- id: The unique identifier for each specific sale transaction.
- date: The calendar date on which the transaction occurred (covering the year 2015).
- time: The specific timestamp of the sale.
- name: The specific variety of pizza sold (e.g., classic_dlx, bbq_ckn).
- size: The size category of the pizza (e.g., L, M).
- type: The broader category or style of the pizza (e.g., classic, supreme).
- price: The currency value/revenue generated from the specific item sold.
Distribution
The dataset is provided in a CSV format with a file size of approximately 3.83 MB. It contains roughly 49,600 valid records (rows), ensuring a robust sample size for statistical modelling. The data structure is consistent with zero mismatched or missing values reported across key columns.
Usage
- Sales Forecasting: Utilise historical data to build regression models (specifically Linear Regression) for predicting future sales volume.
- Customer Preference Analysis: Analyse the popularity of specific pizza types, sizes, and ingredients to inform menu engineering.
- Revenue Optimisation: Investigate price points and peak sales times to develop dynamic pricing strategies.
- Inventory Management: Model ingredient usage based on sales frequency of specific pizza names and types.
Coverage
- Geographic Scope: Includes data from various pizza restaurants and chains across different regions (specific locations not detailed).
- Time Range: The data covers the full calendar year of 2015, from 1st January 2015 to 31st December 2015.
- Demographic Scope: Focuses on transactional sales data rather than specific customer demographics.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Data Scientists: For training regression models and practising predictive analytics.
- Restaurant Owners/Managers: To understand industry benchmarks and sales patterns.
- Business Analysts: For deriving insights into retail food trends and revenue drivers.
- Students: Ideal for beginner to intermediate levels learning data cleaning and visualisation.
Dataset Name Suggestions
- Global Pizza Sales and Revenue 2015
- Pizza Place Transactional History
- Yearly Pizza Market Trends and Sales
- Restaurant Sales Volume and Pricing Data
Attributes
Original Data Source: Yearly Pizza Market Trends and Sales
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