North America registered the highest mobile data consumption per connection in 2023, with the average connection consuming 29 gigabytes per month. This figure is set to triple by 2030, driven by the adoption of data intensive activities such as 4K streaming.

The average time spent daily on a phone, not counting talking on the phone, has increased in recent years, reaching a total of 4 hours and 30 minutes as of April 2022. This figure is expected to reach around 4 hours and 39 minutes by 2024.

This dataset contains information on the prices of several mobile phones from different brands. It includes details such as the storage capacity, RAM, screen size, camera specifications, battery capacity, and price of each device.

Columns

• Brand: the manufacturer of the phone

• Model: the name of the phone model

• Storage (GB): the amount of storage space (in gigabytes) available on the phone

• RAM (GB): the amount of RAM (in gigabytes) available on the phone

• Screen Size (inches): the size of the phone's display screen in inches

• Camera (MP): the megapixel count of the phone's rear camera(s)

• Battery Capacity (mAh): the capacity of the phone's battery in milliampere hours

• Price ($): the retail price of the phone in US dollars

Each row represents a different mobile phone model. The data can be used to analyze pricing trends and compare the features and prices of different mobile phones.

** The purpose of creating this dataset is solely for educational use, and any commercial use is strictly prohibited and this dataset was large language models generated and not collected from actual data sources.

This dataset is collected by DataCluster Labs, India. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai This dataset is an extremely challenging set of over 3000+ original Mobile Phone images captured and crowdsourced from over 1000+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at ****DC Labs.

Dataset Features

Dataset size : 3000+ Captured by : Over 1000+ crowdsource contributors Resolution : 99% images HD and above (1920x1080 and above) Location : Captured with 600+ cities accross India Diversity : Various lighting conditions like day, night, varied distances, view points etc. Device used : Captured using mobile phones in 2020-2021 Applications : Mobile Phone detection, cracked screen detection, etc.

Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record

To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more.

Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

This statistic shows the average monthly wireless data usage per user in the United States by age in the first two quarters of 2018. In the first half of 2018, users 25 years and younger used 4.1 GB of cellular and 16.8 GB of Wi-Fi wireless data.

English(the United States) Scripted Monologue Smartphone speech dataset_Guiding, collected from monologue based on given prompts, covering smart car, smart home, voice assistant domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(344 speakers), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

English(North America) Scripted Monologue Smartphone and PC speech dataset, collected from monologue based on given scripts, covering common expressions. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(302 North American), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Forecast: Mobile Data Usage per Data Capable Device Subscriber in the US 2022 - 2026 Discover more data with ReportLinker!

In 2022, the average data used per smartphone per month worldwide amounted to 15 gigabytes (GB). The source forecasts that this will increase almost four times reaching 46 GB per smartphone per month globally in 2028.

The Alesco Phone ID Database data ties together a consumer's true identity, and with linkage to the Alesco Power Identity Graph, we are perfectly positioned to help customers solve today's most challenging marketing, analytics, and identity resolution problems.

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English(The United States of America) Children Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering essay stories. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers, geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Mobile Phones Dataset (2000 Entries) - Description

Overview

This dataset contains specifications and details for 2,000 mobile phone models from various brands in the year 2000. The data includes comprehensive technical specifications, pricing information, sales platforms, and customer ratings.

File Information

• File Name: mobile_phones_2000.csv
• Format: CSV (Comma-Separated Values)
• Entries: 2,000 mobile phone models
• Columns: 11 attributes per phone model

Data Columns Description

1. Brand: Manufacturer of the phone (e.g., Apple, Samsung, OnePlus, Sony)
2. Model: Specific model name/number of the phone
3. Price (USD): Retail price in US dollars (ranging from $160.34 to $1997.29)
4. Selling Platform: Marketplace where the phone is sold (e.g., Best Buy, Amazon, Official Store)
5. Rating: Customer rating on a 5-point scale (from 3.0 to 5.0)
6. Refresh Rate (Hz): Display refresh rate (60Hz, 90Hz, 120Hz, 144Hz, 165Hz)
7. Screen Size (inches): Diagonal display size (ranging from 5.0" to 7.5")
8. RAM (GB): Memory capacity (4GB, 6GB, 8GB, 12GB, 16GB)
9. Storage (GB): Internal storage capacity (64GB, 128GB, 256GB, 512GB, 1024GB)
10. Processor: Chipset used (e.g., Snapdragon, Dimensity, Exynos, A15 Bionic, Tensor)
11. Camera Setup: Camera configuration (multiple combinations of 2MP-200MP sensors)

Key Observations

• Price Range: Wide price spectrum from budget ($160) to premium ($1997) devices
• Brand Diversity: Includes major brands like Apple, Samsung, Sony, and emerging brands like Realme, Vivo

• Technical Specifications: Shows the evolution of mobile technology in 2000 with:

  • High refresh rate displays (up to 165Hz)
  • Large RAM configurations (up to 16GB)
  • Multi-camera setups (up to 200MP sensors)
  • Varied processor options from different manufacturers

• Sales Channels: Mix of online platforms (Amazon, eBay), electronics retailers (Best Buy), and brand official stores

Potential Use Cases

1. Market Analysis: Study brand positioning and pricing strategies
2. Product Comparison: Compare specifications across brands and models
3. Technology Trends: Analyze the state of mobile technology in 2000
4. Pricing Research: Understand the relationship between specs and pricing
5. Retail Analysis: Examine distribution channels for mobile devices

Data Quality Notes

• The dataset appears comprehensive with complete entries for all 2,000 models
• Contains a mix of realistic and potentially placeholder model names (some combinations seem unusual for the year 2000)
• Some specifications (like 200MP cameras in 2000) may be anachronistic or represent hypothetical/placeholder data

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We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

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Snapshot img

Mobile Phone Market Size 2025-2029

The mobile phone market size is forecast to increase by USD 213.9 billion at a CAGR of 6.8% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing adoption of smartphones. According to recent data, sales of mobile phones, particularly smartphones, through e-commerce platforms have surged, indicating a strong consumer demand. This trend is expected to continue as more consumers shift towards online shopping for convenience and accessibility. However, the market faces challenges related to security and privacy concerns with smartphone usage. With the increasing amount of personal data being stored and transmitted through mobile devices, there is a growing need for robust security measures to protect against cyber threats. Companies in the market must prioritize addressing these concerns through innovative solutions and transparent communication with consumers to build trust and maintain market competitiveness. Effective strategies for navigating these challenges include investing in advanced security features, implementing data protection policies, and providing clear and concise information to consumers about their privacy practices. By focusing on these key drivers and challenges, companies can capitalize on market opportunities and position themselves for long-term success in the market.

What will be the Size of the Mobile Phone Market during the forecast period?

Request Free SampleThe market continues to evolve at an unprecedented pace, with technological advancements and shifting consumer preferences shaping its dynamics. Optical and digital zoom capabilities enhance photographic experiences, while artificial intelligence (AI) and machine learning algorithms elevate user experience (UX) through personalized recommendations and seamless interactions. Mobile gaming gains traction, fueled by improved graphics and processing power. Fingerprint sensors and biometric authentication offer enhanced security, and image stabilization ensures crisp, clear images. Mobile hardware innovations, such as high refresh rates, push the boundaries of performance. The integration of AI, biometric authentication, and UX design continues to redefine mobile design, as mobile data, network, and advertising industries adapt to meet evolving consumer demands. Camera technology, mobile payments, app development, and streaming services further expand the market's reach, with augmented reality (AR) and virtual reality (VR) applications poised to revolutionize industries. Mobile marketing and wireless charging solutions cater to the growing need for convenience and connectivity. The mobile landscape remains a dynamic and ever-evolving ecosystem, with continuous innovation and adaptation shaping its future.

How is this Mobile Phone Industry segmented?

The mobile phone industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Distribution ChannelOfflineOnlineTypeSmartphoneFeature phonePrice-RangeBudgetMid-RangePremiumOperating System AndroidiOSOthersGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalySpainUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Distribution Channel Insights

The offline segment is estimated to witness significant growth during the forecast period.In the dynamic the market, various entities shape consumer behavior and market trends. Social media platforms serve as a powerful tool for mobile marketing, enabling brands to engage with customers and promote their latest offerings. Mobile security remains a top priority, with mobile software providers continuously releasing updates to safeguard against threats. Mobile tariffs vary, offering consumers diverse pricing plans, including pay-as-you-go and monthly subscriptions. Feature phones cater to budget-conscious consumers, while fast charging and long battery life are desirable features for power users. Mobile operating systems, such as Android and iOS, dominate the market, providing a seamless user experience (UX) through mobile design and intuitive mobile apps. Virtual reality (VR) and augmented reality (AR) technologies offer immersive experiences, while optical zoom and digital zoom enhance camera capabilities. Artificial intelligence (AI) and machine learning integrate into mobile hardware, improving functionality and convenience. Biometric authentication, including fingerprint sensors and facial recognition, adds an extra layer of security. Mobile gaming, streaming services, and mobile payments cater to diverse consumer preferences. App development continues to evolve, with mobile advertising and data privacy becoming increasingly important considerations. Mobile service provide

Success.ai’s Phone Number Data offers direct access to over 50 million verified phone numbers for professionals worldwide, extracted from our expansive collection of 170 million profiles. This robust dataset includes work emails and key decision-maker profiles, making it an essential resource for companies aiming to enhance their communication strategies and outreach efficiency. Whether you're launching targeted marketing campaigns, setting up sales calls, or conducting market research, our phone number data ensures you're connected to the right professionals at the right time.

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This data collection is comprised of responses from two sets of survey questionnaires, the basic Current Population Survey (CPS) and a supplemental survey on the topic of cell phone use in the United States, which was primarily administered in February 2004. An additional sample of respondents was given the supplemental survey with the November 2004 CPS. The CPS, administered monthly, is a labor force survey providing current estimates of the economic status and activities of the population of the United States. Specifically, the CPS provides estimates of total employment (both farm and nonfarm), nonfarm self-employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment. The CPS is conducted in approximately 56,000-57,000 households. The Cell Phone supplement contained household-level questions and provides data about household use of regular landline telephones and household use of cell phones. Respondents were specifically asked about the amount of cell phone usage, the number of landlines in the home, the different uses for the landlines (e.g., for computer lines or fax machines), how many members of the household had a working cell phone number, how many cell phones each member of the household had, whether the cell phones were answered by more than one household member, and the number of the calls the household received via cell phone. Demographic variables include age, sex, race, Hispanic origin, marital status, veteran status, educational attainment, occupation, and income.

Forecast: Mobile Data Usage Per Mobile Broadband Subscription in the US 2024 - 2028 Discover more data with ReportLinker!

English(the United States) Spontaneous Dialogue Smartphone speech dataset, collected from dialogues based on given topics, covering generic domain. Transcribed with text content, speaker's ID, gender and other attributes. Our dataset was collected from extensive and diversify speakers(1,416 Americans), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

The population share with mobile internet access in the United States was forecast to continuously increase between 2024 and 2029 by in total 2.7 percentage points. After the ninth consecutive increasing year, the mobile internet penetration is estimated to reach 92.51 percent and therefore a new peak in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.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).

This web map visualizes the prevalence of households in a given geography that do not own a computer, smartphone, or tablet. Data are shown by tract, county, and state boundaries -- zoom out to see data visualized for larger geographies. The map also displays the boundary lines for the jurisdiction of Rochester, NY (visible when viewing the tract level data), as this map was created for a Rochester audience.This web map draws from an Esri Demographics service that is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2014-2018ACS Table(s): B28001, B28002 (Not all lines of ACS table B28002 are available in this feature layer)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 19, 2019National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.