Dark Mode
Smartphone Customer Satisfaction Data
E-commerce & Online Transactions
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This synthetic yet realistic dataset offers insights into smartphone features, customer reviews, and sales data. It includes over 90 customer reviews for six popular smartphone models from leading brands such as Apple, Samsung, and Google. The dataset is designed to help understand how various product specifications influence purchasing decisions and overall customer satisfaction. It combines detailed product specifications, customer star ratings, review texts, and verified purchase status with estimated sales figures per model.
Columns
- model_id (Integer): A unique identifier for each distinct phone model.
- brand (String): The manufacturer of the phone (e.g., "Apple", "Samsung", "Google").
- model_name (String): The specific name of the phone model (e.g., "iPhone 15").
- price (Integer): The retail price of the phone in USD.
- screen_size (Float): The diagonal screen size of the phone in inches.
- battery (Integer): The battery capacity of the phone in mAh.
- camera_main (String): The resolution of the phone's main camera (e.g., "48MP").
- ram (Integer): The amount of RAM (Random Access Memory) in GB.
- storage (Integer): The internal storage capacity in GB.
- has_5g (Boolean): Indicates whether the phone model supports 5G connectivity (TRUE/FALSE).
- water_resistant (String): The water resistance rating, if any (e.g., "IP68" or "None").
- units_sold (Integer): An estimated number of units sold for market analysis purposes.
- review_id (Integer): A unique identifier for each customer review.
- user_name (String): A randomly generated name for the reviewer.
- star_rating (Integer): The customer's rating, ranging from 1 (worst) to 5 (best).
- verified_purchase (Boolean): Indicates whether the reviewer's purchase was verified (TRUE/FALSE).
- review_date (Date): The date when the review was submitted, in YYYY-MM-DD format (e.g., "2023-05-10").
- review_text (String): Simulated text of the customer's review, based on features and rating (e.g., "The 48MP camera is amazing!").
Distribution
The dataset is typically provided in a CSV file format. It comprises over 90 customer review records, along with corresponding smartphone product specifications and sales data for 6 distinct phone models. The exact total number of rows or the specific file size in MB/GB is not specified.
Usage
This dataset is ideal for various analytical applications, including:
- Feature importance analysis: Determining which smartphone specifications (e.g., battery life, camera quality) most significantly influence customer ratings and purchasing decisions.
- Sentiment analysis: Applying Natural Language Processing (NLP) techniques to extract insights and sentiment from customer review texts.
- Pricing strategy optimisation: Analysing the correlation between price and customer satisfaction or sales volume.
- Market research: Comparing performance and customer perception across different brands (e.g., Apple vs. Samsung vs. Google) and models.
- Sales vs. features correlation: Investigating how product features and pricing impact estimated units sold.
Coverage
This dataset has a Global region coverage. It includes data pertaining to six smartphone models from three major brands: Apple (iPhone 14, iPhone 15), Samsung (Galaxy S22, Galaxy S23), and Google (Pixel 7, Pixel 8). The review dates are indicative of data from around 2023. While it includes customer reviews, specific demographic details of the reviewers are not available beyond randomly generated usernames. As a synthetic dataset, it is designed to be realistic for general market analysis.
License
CC0
Who Can Use It
This dataset is suitable for:
- Data Analysts and Scientists: For performing regression analysis, sentiment analysis, and predictive modelling.
- Marketing Professionals: To understand consumer preferences, optimise product features, and refine marketing strategies.
- Product Managers: To inform product development, feature prioritisation, and competitive analysis.
- Market Researchers: To study market trends, brand comparisons, and consumer behaviour in the smartphone industry.
- Academics and Students: For educational purposes and research projects related to consumer electronics, e-commerce, and data analysis.
Dataset Name Suggestions
- Smartphone Customer Satisfaction Data
- Mobile Phone Market & Reviews Dataset
- Consumer Electronics Feature Analysis
- Smartphone Product Performance
- Mobile Device Sales and Reviews
Attributes
Original Data Source: Smartphone Feature Optimization (Marketing Mix)
QUALITY