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Customer Retail Spending Analytics
Retail & Consumer Behavior
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About
This dataset offers valuable insights into customer retail spending behaviour, designed to support a range of analytical activities. It provides key demographic and behavioural data points for customers, enabling retailers and analysts to understand purchasing patterns, tailor marketing initiatives, and optimise product offerings. The data is instrumental for customer segmentation, identifying high-value customers, and informing strategic decisions related to pricing, product design, and store layouts.
Columns
- Customer ID: A unique identifier assigned to each customer for tracking and analytical purposes.
- Gender: The gender of the customer, categorised as male or female. This information assists in tailoring marketing strategies and product offerings to different demographic segments.
- Income: The income level of the customer, typically presented in income brackets or ranges. This data offers insights into purchasing power, helping to determine pricing strategies and product affordability.
- Spending Score (1-100): A numerical score assigned to each customer based on their spending behaviour. This score is derived from factors such as purchase frequency, average transaction value, and total expenditure, aiding in the identification of high-value customers.
- Age: The age of the customer, categorised into age groups or ranges. This demographic information is crucial for tailoring marketing messages and product offerings, as well as influencing decisions on product design, packaging, and store layout to appeal to specific age demographics.
Distribution
The dataset is provided in a CSV format ("Shopping_data.csv") and has a file size of 4.29 kB. It comprises 5 columns and 200 records. All records are valid, with 0% mismatched and 0% missing data across all fields.
- Customer ID: Ranges from 1 to 200, with a mean of 101.
- Gender: Comprises 56% female and 44% male customers.
- Age: Ranges from 18 to 70 years, with a mean age of 38.9 years.
- Annual Income (k$): Ranges from 15 k$ to 137 k$, with a mean annual income of 60.6 k$.
- Spending Score (1-100): Ranges from 1 to 99, with a mean spending score of 50.2.
Usage
This dataset is ideal for:
- Customer segmentation and profiling
- Developing targeted marketing strategies and personalised product offerings
- Informing pricing strategies and assessing product affordability
- Identifying and nurturing high-value customers
- Supporting decisions related to product design, packaging, and store layout
- Data analytics, data cleaning, and machine learning applications such as regression and decision trees.
Coverage
The dataset covers demographic information including gender (male, female), age (18 to 70 years), and income levels (15k$ to 137k$) of customers. Behavioural coverage is provided through the Spending Score (1-99). There is no specific geographic or time range information detailed.
License
CC0: Public Domain
Who Can Use It
- Retailers: To optimise marketing campaigns, refine product assortments, and enhance customer experience.
- Marketing Analysts: For detailed customer segmentation and understanding consumer behaviour.
- Data Scientists: To build predictive models for customer lifetime value, churn, or targeted promotions.
- Business Strategists: To inform overall business strategy, pricing models, and store operations.
- Academics and Researchers: For studies on consumer behaviour and retail analytics.
Dataset Name Suggestions
- Customer Retail Spending Analytics
- Shopping Behaviour Segmentation
- Retail Customer Demographics & Spending
- Consumer Spending Score Data
- Mall Customer Data Analysis
Attributes
Original Data Source: Customer Retail Spending Analytics
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