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Retail Product Pricing and Demand Data
Retail & Consumer Behavior
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Free
About
This dataset is designed to facilitate retail price optimisation by providing historical data to identify the most appropriate price for products or services that maximises company profitability. It helps businesses understand the delicate balance of pricing; setting prices too high can deter customers, while underpricing results in a loss of revenue. The dataset aims to help achieve profit objectives and effectively serve customers by striking the right pricing balance. Factors such as demography, operating costs, and survey data play a role in efficient pricing strategies, which are crucial as pricing mistakes can impact a company's reputation.
Columns
- product_id: Unique identifier for the product.
- product: Name of the product (e.g., health5, health7).
- product_category_name: Category to which the product belongs (e.g., garden_tools, health_beauty).
- month_year: The month and year of the data entry, covering dates from January 2017 to January 2018.
- qty: The quantity of the product sold, with values ranging from 1.00 to 122.00.
- total_price: The total price calculated as quantity sold multiplied by the unit price, ranging from 19.90 to 12095.00.
- freight_price: The average freight price associated with the product, ranging from 0.00 to 79.76.
- unit_price: The average unit price of the product, ranging from 19.90 to 364.00.
- product_name_lenght: The length of the product name, ranging from 29 to 60 characters.
- product_description_lenght: The length of the product description, ranging from 100 to 3006 characters.
- product_photos_qty: The quantity of photos available for the product, ranging from 1 to 8.
- product_weight_g: The weight of the product in grams, ranging from 100 to 9750.
- product_score: The average rating or score of the product, ranging from 3.3 to 4.5.
- customers: The number of customers within the specific product category, ranging from 1 to 339.
- weekday: The number of weekdays in that particular month, ranging from 20 to 23.
- weekend: The number of weekend days in that particular month, ranging from 8 to 10.
- holiday: The number of holidays in that particular month, ranging from 0 to 4.
- month: The month number (1-12).
- year: The year of the data (2017 or 2018).
- s: Seasonality factor, ranging from 0.48 to 100.
- volume: The product volume, ranging from 640.00 to 32736.00.
- comp_1: The price of competitor 1, ranging from 19.90 to 350.
- ps1: The product rating of competitor 1, ranging from 3.7 to 4.5.
- fp1: The freight price of competitor 1, ranging from 0.10 to 57.23.
- comp_2: The price of competitor 2, ranging from 19.90 to 350.
- ps2: The product rating of competitor 2, ranging from 3.3 to 4.4.
- fp2: The freight price of competitor 2, ranging from 4.41 to 57.23.
- comp_3: The price of competitor 3, ranging from 19.90 to 256.
- ps3: The product rating of competitor 3, ranging from 3.5 to 4.4.
- fp3: The freight price of competitor 3, ranging from 7.67 to 57.23.
- lag_price: The previous month's price of the product, ranging from 19.85 to 364.00.
Distribution
The dataset is provided as a CSV data file named
retail_price.csv. It has a file size of 121.4 kB. The dataset is structured with 30 distinct columns and contains 676 records or rows of data.Usage
This dataset is ideal for a variety of analytical applications, including:
- Exploratory Data Analysis to uncover patterns and insights.
- Data Visualisation to present findings clearly.
- Demand Forecasting to predict future product demand.
- Price Optimisation to determine optimal product pricing strategies.
Coverage
The dataset covers a time range primarily from January 2017 to January 2018, with specific date ranges observed for the
month_year column. Geographic and demographic scopes are not explicitly detailed within the dataset's context.License
CC0: Public Domain
Who Can Use It
This dataset is intended for:
- Businesses looking to refine their pricing strategies and maximise profitability.
- Data analysts and data scientists performing tasks related to retail analytics, demand forecasting, and price elasticity.
- Researchers and academics interested in market dynamics, consumer behaviour, and competitive pricing models.
Dataset Name Suggestions
- Retail Product Pricing and Demand Data
- E-commerce Price Optimisation Dataset
- Product Performance and Competitive Pricing
- Sales Demand and Price Analytics
- Retail Business Profitability Data
Attributes
Original Data Source: Retail Product Pricing and Demand Data
QUALITY