Dark Mode
Retail Customer Behaviour Analytics
Retail & Consumer Behavior
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
an essential resource designed to provide valuable insights into the shopping behaviours and demographic profiles of individuals visiting a mall. It is pivotal for businesses seeking to refine their marketing strategies, improve customer interaction, and enhance the overall shopping experience through targeted services and offers. The primary objective is to facilitate the identification of distinct customer segments within the mall's clientele by analysing patterns in age, annual income, spending scores, and gender. Analysing this data enables the development of tailored marketing campaigns and personalised experiences that meet the varied needs of different segments.
Columns
The dataset contains five distinct attributes detailing customer characteristics and behaviour:
- Customer ID: A unique numeric identifier assigned to each individual customer record.
- Gender: Specifies the reported gender of the customer. The data shows a slight majority of Female records (56%) compared to Male records (44%).
- Age: The age of the customer, ranging from 18 years to 70 years.
- Annual Income (k$): The stated annual income of the customer, recorded in thousands of US dollars. In this sample, incomes range from $15k to $137k.
- Spending Score (1-100): A normalised score reflecting the customer’s purchasing and spending behaviour. Scores range from 1 to 99, where a higher score signifies greater spending activity.
Distribution
The dataset focuses on several hundred mall visitors. It is structured as a single flat file, typically in CSV format, containing 200 valid records. There are 5 columns, and all records are complete with no missing values reported. The expected update frequency for this specific set of data is listed as 'Never'.
Usage
This data product is an excellent foundation for data analysis and machine learning projects, including:
- Customer Segmentation: Employing clustering methodologies to group customers based on their features for deeper understanding.
- Targeted Marketing: Developing bespoke marketing campaigns aimed at defined customer segments to boost engagement and sales performance.
- Market Analysis: Gaining clarity on the demographic makeup and purchasing habits of mall visitors to inform strategic business planning.
- Personalisation: Creating enhanced customer experiences through services, recommendations, and offers tailored to individual or segment needs.
Coverage
The data covers demographic variables including Gender, Age (18-70 years), and Annual Income ($15k-$137k), alongside a behavioural Spending Score. Specific geographic or temporal coverage details are not included within this data sample.
License
CC0: Public Domain
Who Can Use It
This resource is ideally suited for professionals and students focused on retail and marketing intelligence:
- Retailers: To understand core customer dynamics and improve product offerings.
- Marketers: To effectively segment audiences and launch highly personalised campaigns.
- Business Analysts: To leverage data-driven insights for strategic decision-making and portfolio projects.
- Data Scientists/Students: For use in exploratory data analysis (EDA), data visualization, and advanced clustering algorithm testing (e.g., Python projects).
Dataset Name Suggestions
- Mall Customer Behaviour Insights
- Retail Customer Profiling Data
- Customer Clustering Data
- Mall Customer Analytics Sample
- Shopper Demographic Segmentation
Attributes
Original Data Source: Retail Customer Behaviour Analytics
QUALITYLoading...