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Global Bike Retail Performance Data
Retail & Consumer Behavior
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About
Captures detailed sales transactions for a bike store chain, featuring 100,000 unique records of bike sales. Each row details a single sales transaction, providing key insights into the customer, specific bike model, financial sales metrics, and store location. The data is highly valuable for advanced retail analysis, including the identification of sales trends, deep dives into customer demographics, and performance evaluation for specific stores and sales personnel. This dataset is designed to provide visibility into the "Pedal to Profits" operational flow.
Columns
- Sale_ID: (Integer) A unique identifier assigned to each sales event.
- Date: (String) The date of the transaction, recorded in DD-MM-YYYY format.
- Customer_ID: (Integer) A unique identifier for the purchasing customer.
- Bike_Model: (String) Specifies the model of the bicycle sold (e.g., BMX, Road Bike).
- Price: (Float) The unit price of the bike sold.
- Quantity: (Integer) The number of bikes included in that specific transaction, ranging from 1 to 5.
- Store_Location: (String) The location of the retail store where the transaction occurred, featuring locations such as New York and Phoenix.
- Salesperson_ID: (Integer) The unique identifier for the employee who facilitated the sale.
- Payment_Method: (String) The method used by the customer to pay (e.g., Apple Pay, Debit Card).
- Customer_Age: (Integer) The age of the customer at the time of purchase, spanning 18 to 70 years old.
- Customer_Gender: (String) The reported gender of the customer, showing a balanced distribution of Female and Male.
Distribution
The data file, typically available as
bike_sales_100k.csv, contains 100,000 unique records across 11 distinct fields. The file size is approximately 7.56 MB. The data structure is clean, with all records reported as valid and zero missing or mismatched values across the core fields. The dataset is expected to be updated on a monthly basis.Usage
This data is an excellent resource for market basket analysis and identifying seasonal or temporal sales trends across different models. It is suitable for building predictive models focused on optimizing pricing strategies and managing inventory levels. The records are highly useful for understanding customer segmentation based on age, gender, and purchase behaviour, as well as evaluating the efficiency and performance of individual store locations.
Coverage
The dataset focuses on retail sales transactions primarily within the United States. Geographically, it includes multiple store locations, such as New York and Phoenix. The time range spans 1,727 unique dates. Demographically, customer ages range from 18 to 70, with customer gender representation being split equally between female and male customers.
License
CC0: Public Domain
Who Can Use It
- Retail Analysts: Seeking to segment customer bases, perform A/B testing on store performance, and forecast future demand.
- Data Scientists: Interested in developing machine learning models for revenue prediction and price elasticity studies.
- Business Development Managers: Evaluating regional sales success and monitoring the effectiveness of individual sales employees.
Dataset Name Suggestions
- 100k Retail Bike Sales Transactions
- US Bike Retail Sales Records
- Pedal to Profits: Sales Analytics
- Global Bike Retail Performance Data
Attributes
Original Data Source: Global Bike Retail Performance Data
QUALITY