Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

In the first quarter of its 2025 fiscal year, Apple generated around ** billion U.S. dollars in revenue from the sales of iPhones. Apple iPhone revenue The Apple iPhone is one of the biggest success stories in the smartphone industry. Since its introduction to the market in 2007, Apple has sold more than *** billion units worldwide. As of the third quarter of 2024, the Apple iPhone’s market share of new smartphone sales was over ** percent. Much of its accomplishments can be attributed to Apple’s ability to keep the product competitive throughout the years, with new releases and updates. Apple iPhone growth The iPhone has shown to be a crucial product for Apple, considering that the iPhone’s share of the company’s total revenue has consistently grown over the years. In the first quarter of 2009, the iPhone sales were responsible for about ********* of Apple’s revenue. In the third quarter of FY 2024, this figure reached a high of roughly ** percent, equating to less than ** billion U.S. dollars in that quarter. In terms of units sold, Apple went from around **** million units in 2010 to about *** million in 2023, but registered a peak in the fourth quarter of 2020 with more than ** million iPhones sold worldwide.

The number of Apple iPhone unit sales dramatically increased between 2007 and 2023. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around *** million smartphones. By 2023, this number reached over *** million units. The newest models and iPhone’s lasting popularity Apple has ventured into its 17th smartphone generation with its Phone ** lineup, which, released in September 2023, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A16 bionic chip and running on iOS **, these models present improved displays, cameras, and functionalities. On the one hand, such features come, however, with hefty price tags, namely, an average of ***** U.S. dollars. On the other hand, they contribute to making Apple among the leading smartphone vendors worldwide, along with Samsung and Xiaomi. In the first quarter of 2024, Samsung shipped over ** million smartphones, while Apple recorded shipments of roughly ** million units. Success of Apple’s other products Apart from the iPhone, which is Apple’s most profitable product, Apple is also the inventor of other heavy-weight players in the consumer electronics market. The Mac computer and the iPad, like the iPhone, are both pioneers in their respective markets and have helped popularize the use of PCs and tablets. The iPad is especially successful, having remained as the largest vendor in the tablet market ever since its debut. The hottest new Apple gadget is undoubtedly the Apple Watch, which is a line of smartwatches that has fitness tracking capabilities and can be integrated via iOS with other Apple products and services. The Apple Watch has also been staying ahead of other smart watch vendors since its initial release and secures around ** percent of the market share as of the latest quarter.

This synthetic but realistic dataset contains 90+ customer reviews for 6 smartphone models (from Apple, Samsung, and Google), along with: - Product specifications (Price, Screen Size, Battery, Camera, RAM, Storage, 5G, Water Resistance) - Customer reviews (Star Ratings, Review Text, Verified Purchase Status) - Sales data (Units Sold per Model)

Potential Use Cases: ✅ Feature importance analysis (Which specs drive ratings?) ✅ Sentiment analysis (NLP on reviews) ✅ Pricing strategy optimization ✅ Market research (Comparing Apple vs. Samsung vs. Google)

Smartphone Customer Satisfaction Survey

Objective: Understand how product features influence purchasing decisions and satisfaction.

Section 1: Demographic & Purchase Behavior

1. Which smartphone brand did you purchase?

   • ☐ Apple
   • ☐ Samsung
   • ☐ Google
   • Maps to brand column.

2. Which model did you purchase?

   • Apple: ☐ iPhone 14 | ☐ iPhone 15
   • Samsung: ☐ Galaxy S22 | ☐ Galaxy S23
   • Google: ☐ Pixel 7 | ☐ Pixel 8
   • Maps to model_name column.

3. Where did you purchase the phone?

   • ☐ Online (e.g., Amazon, Brand Website)
   • ☐ Physical Store
   • Justifies verified_purchase (assumed online = verified).

Section 2: Product Feature Ratings

1. How would you rate the following features? (1 = Poor, 5 = Excellent)

   • Battery Life: ⭐⭐⭐⭐⭐
   • Camera Quality: ⭐⭐⭐⭐⭐
   • Screen Size: ⭐⭐⭐⭐⭐
   • Performance (RAM/Processor): ⭐⭐⭐⭐⭐
   • Aggregates into star_rating (average of these).

2. Which feature is MOST important to you?

   • ☐ Battery Life
   • ☐ Camera Quality
   • ☐ Screen Size
   • ☐ Performance
   • ☐ Price
   • Explains review_text keywords (e.g., "battery" mentions).

Section 3: Price & Satisfaction

1. How do you feel about the price of your phone?

   • ☐ Very Affordable
   • ☐ Fairly Priced
   • ☐ Slightly Expensive
   • ☐ Too Expensive
   • Maps to price vs. star_rating correlation.

2. Would you recommend this phone to others?

   • ☐ Definitely Yes
   • ☐ Probably Yes
   • ☐ Neutral
   • ☐ Probably No
   • ☐ Definitely No
   • Linked to star_rating (5 = Definitely Yes).

Column Details (Metadata)

Column Name (Type) Description "Example"**

model_id (Integer) Unique ID for each phone model 1 (iPhone 14)

brand (String) Manufacturer (Apple, Samsung, Google) "Apple"

model_name (String) Name of the phone model "iPhone 15"

price (Integer) Price in USD 999

screen_size (Float) Screen size in inches 6.1

battery (Integer) Battery capacity in mAh 4000

camera_main (String) Main camera resolution (MP) "48MP"

ram (Integer) RAM in GB 8

storage (Integer) Storage in GB 128

has_5g (Boolean) Whether the phone supports 5G TRUE

water_resistant (String) Water resistance rating (IP68 or None) "IP68"

units_sold (Integer) Estimated units sold (for market analysis) 15000

review_id (Integer) Unique ID for each review 1

user_name (String) Randomly generated reviewer name "John"

star_rating (Integer) Rating from 1 (worst) to 5 (best) 5

verified_purchase (Boolean) Whether the reviewer bought the product TRUE

review_date (Date) Date of the review (YYYY-MM-DD) "2023-05-10"

review_text (String) Simulated review text based on features & rating "The 48MP camera is amazing!"

Suggested Analysis Ideas to inspire data analysis: A. Feature Impact on Ratings Regression: star_rating ~ battery + camera_main + price Key drivers: Does battery life affect ratings more than camera quality?

B. Sentiment Analysis (NLP) Use tidytext (R) or NLTK (Python) to extract most-loved/hated features. Example: r library(tidytext) reviews_tidy <- final_data %>% unnest_tokens(word, review_text)
reviews_tidy %>% count(word, sort = TRUE) %>% filter(n > 5)

C. Brand Comparison Apple vs. Samsung vs. Google: Which brand has higher average ratings? Price sensitivity: Do cheaper phones (e.g., Pixel) get better value ratings?

D. Sales vs. Features Correlation: units_sold ~ price + brand Premium segment analysis: Do iPhones sell more despite higher prices?

Description

UTKPAD is a replay attack database prepared for face age verification as part of the paper "Vulnerability of Face Age Verification to Replay Attacks" published in ICASSP 2024 conference. This database is originated from the well-known large dataset, UTKFace of face images with age labels ranging from 1 to 100 plus years old. Replay attack are recorded with three different mobiles phones: Apple iPhone 12, Samsung Galaxy S9 and Huawei Mate 30. With UTKPAD database, we also provide file lists that can be used for training and testing of replay attacks detection and for vulnerability assessment of age verification systems.

The face images in the UTKFace are first enhanced with a face restoration CodeFormer method. Each enhanced image is converted into a video clip with a subsequent concatenation process to have one video clip including all image-to-video converted files. The final video clip is then replayed on Apple iPad Pro in order to record it with three different mobiles phones: Apple iPhone 12, Samsung Galaxy S9 and Huawei Mate 30. And, finally, the recordings are de-concatenated/segmented into sub video clips and each video clip is sampled by taking the middle frame to construct the database of replay attack images.

To avoid breaching the copyright, we release the dataset in the form of deltas that are not actual images. The attack images can be recovered only if the user also obtains and downloads the original UTKFace dataset. When UTKFace is downloaded, however, the attack images of UTKPAD can be easily computed using the script we provide.

Reference

If you're using this dataset, please cite the following publication

@INPROCEEDINGS{Korshunov_ICASSP_2024, author = {Korshunov, Pavel and George, Anjith and {"O}zbulak, G{"o}khan and Marcel, S{\'{e}}bastien}, projects = {Idiap, Biometrics Center}, title = {Vulnerablity of Face Age Verification to Replay Attacks}, booktitle = {ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {2024},}

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)

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The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

In the fourth quarter of 2024, Samsung shipped around 52 million smartphones, a decrease from the both the previous quarter and the same quarter of the previous year. Samsung’s sales consistently place the smartphone giant among the top three smartphone vendors in the world, alongside Xiaomi and Apple. Samsung smartphone sales – how many phones does Samsung sell? Global smartphone sales reached over 1.2 billion units during 2024. While the global smartphone market is led by Samsung and Apple, Xiaomi has gained ground following the decline of Huawei. Together, these three companies hold more than 50 percent of the global smartphone market share.

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users in 2029. Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Thailand and Indonesia.

The number of mobile broadband connections in the Philippines was forecast to continuously increase between 2024 and 2029 by in total 18.3 million connections (+20.46 percent). After the ninth consecutive increasing year, the number of connections is estimated to reach 107.69 million connections and therefore a new peak in 2029. Mobile broadband connections include cellular connections with a download speed of at least 256 kbit/s (without satellite or fixed-wireless connections). Cellular Internet-of-Things (IoT) or machine-to-machine (M2M) connections are excluded. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of mobile broadband connections in countries like Vietnam and Laos.

The smartphone penetration in the Philippines was forecast to continuously decrease between 2024 and 2029 by in total 6.4 percentage points. According to this forecast, in 2029, the penetration will have decreased for the fourth consecutive year to 65.75 percent. The penetration rate refers to the share of the total population.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smartphone penetration in countries like Laos and Malaysia.