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

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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.

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The Product Comparison dataset for online shopping is a new, manually annotated dataset with about 15K human generated sentences, which compare related products based on one or more of their attributes (the first such data we know of for product comparison). It covers ∼8K product sets, their selected attributes, and comparison texts.

Analysis of ‘ICT17 - Individuals who use the internet classified by types of purchases made online during the last 3 months’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/369667dd-e771-4d49-8849-ddd5a14d76d2 on 19 January 2022.

--- Dataset description provided by original source is as follows ---

Individuals who use the internet classified by types of purchases made online during the last 3 months

--- Original source retains full ownership of the source dataset ---

This is a dataset obtained from an online survey conducted in August 2020.

In the survey, participants were introduced to the concept of a smartphone-based shopping assistant application with the help of pictures and videos when shopping with and without the application. Participants were presented with three different shopping scenarios. In each scenario, we showed products on a shelf (groceries, luxury chocolate, shoes, books). The first shopping scenario was a regular shopping scenario (RSS), the second was an augmented reality shopping scenario (ARSS), and the third was an augmented reality shopping scenario with explainable AI features (XARSS). For each scenario participants had to answer questions about how they perceived the scenario and how it influenced their overall purchase intention.

The present work was conducted within the Innovative Training Network project PERFORM funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 765395. The EU Research Executive Agency is not responsible for any use that may be made of the information it contains.

Graph and download economic data for E-Commerce Retail Sales as a Percent of Total Sales (ECOMPCTNSA) from Q4 1999 to Q1 2025 about e-commerce, retail trade, percent, sales, retail, and USA.

The percentage of respondents who report that in the past year, they used a mobile phone or the Internet to make a payment, make a purchase, or to send or receive money through their account.

Percentage of enterprises that receive orders or make sales of goods or services over the Internet, and percentage of enterprises that order goods or services over the Internet, by the North American Industry Classification System (NAICS) and size of enterprise.

The provided dataset appears to be a sales dataset from a company called "**T-Mart.**" The dataset contains various columns with information about the sales transactions, including the date of the transaction, product details, quantity, sales type, location, payment mode, product category, unit of measurement (UOM), purchase price, and some additional labels and counts.

Based on the given information, here's a brief description of the dataset:

The "T-Mart" sales dataset captures sales transactions with details such as the transaction date, unique product identifier (PRODUCT ID), quantity sold, sales type (Direct Sales, Online, etc.), sales location (e.g., California, Alabama), payment mode (Cash, Online), product details (PRODUCT, CATEGORY, UOM), purchase price, and some additional label-based information.

This dataset provides insights into various aspects of the company's sales operations, including the distribution of sales across different categories, products, and locations, as well as information about the payment modes used for transactions.

Analyzing this dataset can help identify trends, popular products, sales performance by location, and preferred payment methods. It's essential for understanding the company's sales dynamics and making informed business decisions.

This dataset appears to be rich in information, and with the right data visualization techniques, we can uncover valuable insights that can be used for strategic planning and optimizing sales strategies.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_c98105aa8d0585e55e44cd3d2c3384dd/view

ICT17 - Individuals who use the internet classified by types of purchases made online during the last 3 months. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Individuals who use the internet classified by types of purchases made online during the last 3 months...

This refers to the number of in-scope businesses placing orders over the Internet as a proportion of the total number of in-scope businesses. It includes orders placed via the Internet whether payment was made online or not: via websites, specialized Internet marketplaces, extranets, EDI over the Internet, smartphone applications, and email. It excludes orders that were cancelled or not completed. For more details, see description of indicator B8 at https://www.itu.int/en/ITU-D/Statistics/Documents/coreindicators/Core-List-of-Indicators_March2022.pdf

This database automatically includes metadata, the source of which is the GOVERNMENT OF THE REPUBLIC OF SLOVENIA STATISTICAL USE OF THE REPUBLIC OF SLOVENIA and corresponding to the source database entitled “Number of online purchases made by individuals in the last 12 months, by country of seller and by method of payment, by education and sex, Slovenia, 2008-2019”.

Actual data are available in Px-Axis format (.px). With additional links, you can access the source portal page for viewing and selecting data, as well as the PX-Win program, which can be downloaded free of charge. Both allow you to select data for display, change the format of the printout, and store it in different formats, as well as view and print tables of unlimited size, as well as some basic statistical analyses and graphics.

Percentage of total sales made online in 2019 and 2020, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.

Analysis of ‘Transactional Retail Dataset of Electronics Store’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/muhammadshahrayar/transactional-retail-dataset-of-electronics-store on 14 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset contains information about an online electronic store. The store has three warehouses from which goods are delivered to customers.

Columns Description

• order_id: A unique id for each order
• customer_id: A unique id for each customer
• date: The date the order was made, given in YYYY-MM-DD format
• nearest_warehouse: A string denoting the name of the nearest warehouse to the customer
• shopping_cart: A list of tuples representing the order items: the first element of the tuple is the item ordered, and the second element is the quantity ordered for such item.
• order_price: A float denoting the order price in USD. The order price is the price of items before any discounts and/or delivery charges are applied.
• delivery_charges: A float representing the delivery charges of the order
• customer_lat: Latitude of the customer’s location
• customer_long: Longitude of the customer’s location
• coupon_discount: An integer denoting the percentage discount to be applied to the order_price.
• order_total: A float denoting the total of the order in USD after all discounts and/or delivery charges are applied.
• season: A string denoting the season in which the order was placed.
• is_expedited_delivery: A boolean denoting whether the customer has requested an expedited delivery
• distance_to_nearest_warehouse: A float representing the arc distance, in kilometres, between the customer and the nearest warehouse to him/her.
• latest_customer_review: A string representing the latest customer review on his/her most recent order
• is_happy_customer: A boolean denoting whether the customer is a happy customer or had an issue with his/her last order.

Inspiration

Use this dataset to perform graphical and/or non-graphical EDA methods to understand the data first and then find and fix the data problems. - Detect and fix errors in dirty_data.csv - Impute the missing values in missing_data.csv - Detect and remove Anolamies - To check whether a customer is happy with their last order

All the Best

--- Original source retains full ownership of the source dataset ---

Dataset Description

The E-commerce Order Dataset provides comprehensive information related to orders, items within orders, customers, payments, and products for an e-commerce platform. This dataset is structured with multiple tables, each containing specific information about various aspects of the e-commerce operations.

Dataset Features

Orders Table:

• order_id: Unique identifier for an order, acting as the primary key.
• customer_id: Unique identifier for a customer. This table may not be unique at this level.
• order_status: Indicates the status of an order (e.g., delivered, cancelled, processing, etc.).
• order_purchase_timestamp: Timestamp when the order was made by the customer.
• order_approved_at: Timestamp when the order was approved from the seller's side.
• order_delivered_timestamp: Timestamp when the order was delivered at the customer's location.
• order_estimated_delivery_date: Estimated date of delivery shared with the customer while placing the order.

Order Items Table

• order_id: Unique identifier for an order.
• order_item_id: Item number in each order, acting as part of the primary key along with the order_id.
• product_id: Unique identifier for a product.
• seller_id: Unique identifier for the seller.
• price: Selling price of the product.
• shipping_charges: Charges associated with the shipping of the product.

Customers Table

• customer_id: Unique identifier for a customer, acting as the primary key.
• customer_zip_code_prefix: Customer's Zip code.
• customer_city: Customer's city.
• customer_state: Customer's state.

Payments Table

• order_id: Unique identifier for an order.
• payment_sequential: Provides information about the sequence of payments for the given order.
• payment_type: Type of payment (e.g., credit_card, debit_card, etc.).
• payment_installments: Payment installment number in case of credit cards.
• payment_value: Transaction value.

Products Table

• product_id: Unique identifier for each product, acting as the primary key.
• product_category_name: Name of the category the product belongs to.
• product_weight_g: Product weight in grams.
• product_length_cm: Product length in centimeters.
• product_height_cm: Product height in centimeters.
• product_width_cm: Product width in centimeters.

The dataset supports measure S.D.4.a of SD23. The Austin Municipal Court offers services via in person, phone, mail, email, online, in the community, in multiple locations, and during non-traditional hours to make it easier and more convenient for individuals to handle court business. This measure tracks the percentage of customers that utilize court services outside of normal business hours, defined as 8am-5pm Monday-Friday, and how many payments were made by methods other than in person. This measure helps determine how Court services are being used and enables the Court to allocate its resources to best meet the needs of the public. Historically, almost 30% of the operational hours are outside of traditional hours and the average percentage of payments made by mail and online has been over 59%. View more details and insights related to this measure on the story page: https://data.austintexas.gov/stories/s/c7z3-geii Data source: electronic case management system and manual tracking of payments received via mail. Calculation: Business hours are manually calculated annually. - A query is run from the court’s case management system to calculate how many monetary transactions were posted. S.D.4.a: Numerator: Number of payments received by mail is entered manually by the Customer Service unit that processes all incoming mail. S.D.4.a Denominator: Total number of web payments is calculated using a query to calculate a total number of payments with a payment type ‘web’ in the case management system. Measure time period: Annual (Fiscal Year) Automated: No Date of last description update: 4/10/2020

Percentage and average percentage of total sales made online in 2023, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.

The percentage of respondents who report using mobile money, a debit or credit card, or a mobile phone to make a payment from an account, or report using the internet to pay bills or to buy something online, in the past 12 months. It also includes respondents who report paying bills or sending remittances directly from a financial institution account or through a mobile money account in the past 12 months

This table contains figures about online shopping, the type of purchases made and characteristics of purchases, such as frequency and amount of purchases. The data can be broken down into various personal characteristics, such as gender, age, education level, employment position and income group. Data available from 2012 to 2019. Status of the figures: The figures in this table are final. Changes as of November 18, 2020: None, this table has been discontinued. When will new numbers come out? Not applicable anymore.

Disposal of boxes from online purchases by Canadian households.