This dataset contains simulated customer data that can be used for segmentation analysis. It includes demographic and behavioral information about customers, which can help in identifying distinct segments within the customer base. This can be particularly useful for targeted marketing strategies, improving customer satisfaction, and increasing sales.
Data is collected from Kaggle. Here is the link of dataset https://www.kaggle.com/datasets/shreyanshverma27/online-sales-dataset-popular-marketplace-data
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The purpose of the analysis: is to get useful information from data. Here we are analyzing Customer Segmentation Data for Marketing Analysis . The basic purpose of this analysis is to get answers of some business questions from this data set that can be helpful in future for success of a marketplace.
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This project starts with exploring and cleaning a dataset to prepare it for analysis. The data exploration process involved identifying and understanding the characteristics of the data, such as the data types, missing values, and distributions of values. The data cleaning process involved detecting and resolving any issues in the data, such as errors, missing values, or duplicate records and remove=ing outliers.
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Once the data has been cleaned and prepared, we will create visualizations graphs and check the relationship between different variables of the dataset.
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We will perform exploratory data analysis (EDA) of this dataset that consists of three steps (1) Univariate Analysis (2) Bivariate Analysis (3) Multivariate Analysis.
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Age: As we see from graph the distribution is roughly uniform, indicating a wide range of ages among customers.
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Income The distribution is right-skewed, with a higher frequency of customers in the lower income ranges.
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Spending Score The distribution is right-skewed, with a higher frequency of customers in the lower income ranges.
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Membership Years The distribution is left-skewed, with many customers having fewer years of membership.
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Purchase Frequency The distribution is right-skewed, indicating that most customers have lower purchase frequencies.
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Last Purchase Amount he distribution is right-skewed, with most customers making smaller purchases.
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Gender: Most Customers are male
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Preferred Category Most Categories of Products are Sports and Electronics
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Certain categories like Clotihing have higher average spending scores.
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Most of the purchase was made in last year by Males.