1 dataset found
  1. Customer Shopping Trends Dataset

    • kaggle.com
    Updated Oct 5, 2023
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    Sourav Banerjee (2023). Customer Shopping Trends Dataset [Dataset]. https://www.kaggle.com/datasets/iamsouravbanerjee/customer-shopping-trends-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 5, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Sourav Banerjee
    Description

    Context

    The Customer Shopping Preferences Dataset offers valuable insights into consumer behavior and purchasing patterns. Understanding customer preferences and trends is critical for businesses to tailor their products, marketing strategies, and overall customer experience. This dataset captures a wide range of customer attributes including age, gender, purchase history, preferred payment methods, frequency of purchases, and more. Analyzing this data can help businesses make informed decisions, optimize product offerings, and enhance customer satisfaction. The dataset stands as a valuable resource for businesses aiming to align their strategies with customer needs and preferences. It's important to note that this dataset is a Synthetic Dataset Created for Beginners to learn more about Data Analysis and Machine Learning.

    Content

    This dataset encompasses various features related to customer shopping preferences, gathering essential information for businesses seeking to enhance their understanding of their customer base. The features include customer age, gender, purchase amount, preferred payment methods, frequency of purchases, and feedback ratings. Additionally, data on the type of items purchased, shopping frequency, preferred shopping seasons, and interactions with promotional offers is included. With a collection of 3900 records, this dataset serves as a foundation for businesses looking to apply data-driven insights for better decision-making and customer-centric strategies.

    Dataset Glossary (Column-wise)

    • Customer ID - Unique identifier for each customer
    • Age - Age of the customer
    • Gender - Gender of the customer (Male/Female)
    • Item Purchased - The item purchased by the customer
    • Category - Category of the item purchased
    • Purchase Amount (USD) - The amount of the purchase in USD
    • Location - Location where the purchase was made
    • Size - Size of the purchased item
    • Color - Color of the purchased item
    • Season - Season during which the purchase was made
    • Review Rating - Rating given by the customer for the purchased item
    • Subscription Status - Indicates if the customer has a subscription (Yes/No)
    • Shipping Type - Type of shipping chosen by the customer
    • Discount Applied - Indicates if a discount was applied to the purchase (Yes/No)
    • Promo Code Used - Indicates if a promo code was used for the purchase (Yes/No)
    • Previous Purchases - The total count of transactions concluded by the customer at the store, excluding the ongoing transaction
    • Payment Method - Customer's most preferred payment method
    • Frequency of Purchases - Frequency at which the customer makes purchases (e.g., Weekly, Fortnightly, Monthly)

    Structure of the Dataset

    https://i.imgur.com/6UEqejq.png" alt="">

    Acknowledgement

    This dataset is a synthetic creation generated using ChatGPT to simulate a realistic customer shopping experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world customer shopping behavior. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation in the context of consumer preferences and retail scenarios.

    Cover Photo by: Freepik

    Thumbnail by: Clothing icons created by Flat Icons - Flaticon

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Sourav Banerjee (2023). Customer Shopping Trends Dataset [Dataset]. https://www.kaggle.com/datasets/iamsouravbanerjee/customer-shopping-trends-dataset
Organization logo

Customer Shopping Trends Dataset

Journey into Consumer Insights and Retail Evolution with Synthetic Data

Explore at:
32 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Oct 5, 2023
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Sourav Banerjee
Description

Context

The Customer Shopping Preferences Dataset offers valuable insights into consumer behavior and purchasing patterns. Understanding customer preferences and trends is critical for businesses to tailor their products, marketing strategies, and overall customer experience. This dataset captures a wide range of customer attributes including age, gender, purchase history, preferred payment methods, frequency of purchases, and more. Analyzing this data can help businesses make informed decisions, optimize product offerings, and enhance customer satisfaction. The dataset stands as a valuable resource for businesses aiming to align their strategies with customer needs and preferences. It's important to note that this dataset is a Synthetic Dataset Created for Beginners to learn more about Data Analysis and Machine Learning.

Content

This dataset encompasses various features related to customer shopping preferences, gathering essential information for businesses seeking to enhance their understanding of their customer base. The features include customer age, gender, purchase amount, preferred payment methods, frequency of purchases, and feedback ratings. Additionally, data on the type of items purchased, shopping frequency, preferred shopping seasons, and interactions with promotional offers is included. With a collection of 3900 records, this dataset serves as a foundation for businesses looking to apply data-driven insights for better decision-making and customer-centric strategies.

Dataset Glossary (Column-wise)

  • Customer ID - Unique identifier for each customer
  • Age - Age of the customer
  • Gender - Gender of the customer (Male/Female)
  • Item Purchased - The item purchased by the customer
  • Category - Category of the item purchased
  • Purchase Amount (USD) - The amount of the purchase in USD
  • Location - Location where the purchase was made
  • Size - Size of the purchased item
  • Color - Color of the purchased item
  • Season - Season during which the purchase was made
  • Review Rating - Rating given by the customer for the purchased item
  • Subscription Status - Indicates if the customer has a subscription (Yes/No)
  • Shipping Type - Type of shipping chosen by the customer
  • Discount Applied - Indicates if a discount was applied to the purchase (Yes/No)
  • Promo Code Used - Indicates if a promo code was used for the purchase (Yes/No)
  • Previous Purchases - The total count of transactions concluded by the customer at the store, excluding the ongoing transaction
  • Payment Method - Customer's most preferred payment method
  • Frequency of Purchases - Frequency at which the customer makes purchases (e.g., Weekly, Fortnightly, Monthly)

Structure of the Dataset

https://i.imgur.com/6UEqejq.png" alt="">

Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic customer shopping experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world customer shopping behavior. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation in the context of consumer preferences and retail scenarios.

Cover Photo by: Freepik

Thumbnail by: Clothing icons created by Flat Icons - Flaticon

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