Businesses are always looking for a way to appeal to more customers. The best way to do this, is by having a thorough understanding of their customers. It may be difficult for a business to decide which type of customer they should appeal to.
The "Shop Customer Data" dataset on Kaggle shows provides data for an imaginative shop's customers. The goal of this project is to finding out which types of customers are ideal for a business.
This will help businesses appeal to certain types of customers and improve their business strategy. By understanding their ideal customer, business can create catered advertisements, incentives, and more.
I will use the following dataset to observe the characteristics of shop customers.
Dataset: https://www.kaggle.com/datasets/datascientistanna/customers-dataset
The columns of the dataset include:
| CustomerID | Gender | Age | Annual Income($) | Spending Score(1-100) | Profession | Work Experience(Years) | Family Size |
|---|---|---|---|---|---|---|---|
| 1 | Male | 19 | 150000 | 39 | Healthcare | 1 | 4 |
| 2 | Male | 21 | 350000 | 81 | Engineer | 3 | 3 |
| 3 | Female | 20 | 860000 | 6 | Engineer | 1 | 1 |
| 4 | Female | 23 | 59000 | 77 | Lawyer | 0 | 2 |
| 5 | Female | 31 | 38000 | 40 | Entertainment | 2 | 6 |
The project will use the above attributes to analyze which type of customer is most beneficial for businesses.
This project aims to use a clustering method. I will use a clustering method to find groups of customers that have similar attributes. Then, I will analyze which of these groups are most ideal for businesses.