As of February 2025, 47.3 percent of LinkedIn users worldwide were between the ages of 25 and 34 years old. In comparison, users older than 55 years old made up 3.3 percent of the social platform's audience. Furthermore, individuals belonging to the 18- to 24-year-old age group constituted 28.7 percent of the professional social network's user base, and 56.9 percent of global users identified as male. Leading LinkedIn audiences As of April 2024, the United States was home to 230 million LinkedIn users, which made it the country with the largest audience of the online network. India had 130 million users, and Brazil had 71 million users. As of June 2024, 16 percent of social media users in the United States utilized LinkedIn, making it the eighth most popular social network in the country. The growing number of LinkedIn subscribers Users of LinkedIn can upgrade to premium accounts to make the most of additional features and tools to help them with their professional networking. It is estimated that there were 174.5 million premium users on LinkedIn in 2023, up from around 154.4 million in 2022. In 2019, there were around 94 million premium users of LinkedIn, indicating an increase in paid subscribers of around 85 percent over the past four years.

LinkedIn is the world’s preeminent social network for professionals. Members create CVs, list their current and previous job roles, skills and education. The business network is also a recruiting...

The number of LinkedIn users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 1.5 million users (+4.51 percent). After the eighth consecutive increasing year, the LinkedIn user base is estimated to reach 34.7 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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).

The number of LinkedIn users in the United States was forecast to continuously increase between 2024 and 2028 by in total **** million users (+**** percent). After the ninth consecutive increasing year, the LinkedIn user base is estimated to reach ****** million users and therefore a new peak in 2028. Notably, the number of LinkedIn users of was continuously increasing over the past years.User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 *** 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 LinkedIn users in countries like Canada and Mexico.

There are over 875 million registered users on LinkedIn in 2024.

There are more male LinkedIn users than females – although it is pretty balanced.

LinkedIn had 27.4 million users in Canada as of January 2025, and more than 43 percent of these users were aged between 25 and 34 years. Users aged 18 to 24 years made up 23 percent of all users of LinkedIn in Canada, and more than 27 percent of users were aged between 35 and 54 years.

Factori houses an extensive dataset of US Person data, providing valuable insights into individuals across various demographic and behavioral dimensions. Our US Person Data section is dedicated to helping you understand the breadth and depth of the information available through our API.

Data Collection and Aggregation Our Person data is gathered and aggregated through surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points. This ensures that the data you access is up-to-date and accurate.

Here are some of the data categories and attributes we offer within US Person Graph: - Geography: City, State, ZIP, County, CBSA, Census Tract, etc. - Demographics: Gender, Age Group, Marital Status, Language, etc. - Financial: Income Range, Credit Rating Range, Credit Type, Net Worth Range, etc. - Persona: Consumer type, Communication preferences, Family type, etc. - Interests: Content, Brands, Shopping, Hobbies, Lifestyle, etc. - Household: Number of Children, Number of Adults, IP Address, etc. - Behaviors: Brand Affinity, App Usage, Web Browsing, etc. - Firmographics: Industry, Company, Occupation, Revenue, etc. - Retail Purchase: Store, Category, Brand, SKU, Quantity, Price, etc.

Here's the data schema: Person_id first_name last_name gender age year month day full_address city state zipcode zip4 delivery_point_bar_code carrier_route walk_sequence_code fips_state_code fips_county_code country_name latitude longtitude address_type metropolitan_statistical_area core_based_statistical_area census_tract census_block
census_block_group primary_address pre_address street post_address address_suffix address_secondline address_abrev
census_median_home_value home_market_value property_build_year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type household_id census_median_household_income
household_size occupation_home_office
dwell_type household_income marital_status length_of_residence number_of_kids pre_school_kids single_parent working_women_in_house_hold homeowner children
adults generations net_worth education_level education_history occupation occuptation_business_owner credit_lines credit_card_user newly_issued_credit_card_user credit_range_new credit_cards loan_to_value and alot more...

52 million people use LinkedIn every week to search for new jobs.

Success.ai presents an unmatched opportunity with its User Profiles Data, offering in-depth access to LinkedIn profiles and company data that empowers businesses to develop ideal customer profiles, enrich company data, and sharpen competitive intelligence. Our LinkedIn Data Solutions are crafted to support your B2B strategies, providing a foundation for sales data enrichment and strategic market positioning.

• User Profiles Data: Utilize detailed individual data for building precise customer profiles.
• LinkedIn Data: Leverage the vast network of LinkedIn for enriched business insights.
• LinkedIn Profile Data: Gain specific details from LinkedIn profiles to inform your marketing and sales strategies.
• LinkedIn Company Data: Dive deep into company specifics to enhance your competitive edge.
• Company Data: Access broad data sets for comprehensive market analysis and business planning.

Key Use Cases:

• Ideal Customer Profile Development: Craft detailed profiles that target the very core of your prospective markets.
• Company Data Enrichment: Augment your existing databases with enriched factual data from LinkedIn.
• Competitive Intelligence: Stay ahead by understanding market dynamics and competitor movements.
• Sales Data Enrichment: Enrich your sales strategies with actionable insights and data points.
• B2B Data Enrichment: Leverage enriched data to optimize your B2B processes and customer interactions.

Why Success.ai is the Preferred Choice:

• Data Precision: With a commitment to 99% accuracy, our data is constantly updated and verified.
• Global Reach: Spanning 195 countries, our data solutions are as international as your business needs.
• Adaptive Data Solutions: Customizable data options tailored to fit the specific needs of your business operations.
• Legal Compliance: All data handling is GDPR-compliant, ensuring ethical practices in data usage.
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By choosing Success.ai, you gain access to a wealth of LinkedIn and user profile data that will enhance your market understanding, enrich customer interactions, and enable effective competitive strategies. Our extensive databases are the cornerstone of successful B2B engagements and strategic business planning.

Get Started with Success.ai Now: Explore the potential of detailed LinkedIn data in your business strategy. Reach out to us for a consultation or start integrating our tailored data solutions today.

And no one beats us on price. Period.

Context

LinkedIn is a place for increasing connection, showing your skills and achievements. Therefore in order to understand the various features like promotions, regional analysis and facial characteristics. This data is taken into consideration.

Content

Data is consisting of around 15000 profiles. The data set deals with a lot of features like region, the way the images are being uploaded, the emotions on them and growth of the users over time.

Lets understand the following attributes for the betterment:-

User id is a thing of privacy and should not be disclosed although there characteristics can be given in order to understand the various behavior pattern of people in LinkedIn. c id : name for each data, basically forms the primary key.

Profession Columns avg time in previous position: The amount of time spent in years in the previous position avg current position length: The amount of time on an average the user is present in the current position avg previous position length: The amount of time on an average the user is present in the previous position m urn: The user id for each profile m urn id: This is reduced to a distinct code no of promotions: Total number of times the user was promoted no of previous positions: The number of previous positions the user holds current position length: The number of months the person is in current position age: The Age of the person gender: Male or Female ethnicity: The percentage of ethnicity n followers: Number of followers

Image Clarity beauty: The beauty is the index for the analysis of the beauty female: This predicts the user image is more to be female or not.
beauty male: This predicts the user image is more to be male or not. blur: The degree of shadiness of the image

Emotion Captured emo anger: The percentage of anger found emo disgust: The percentage of disgust found emo fear : The percentage of fear found emo happiness: The percentage of happiness found emo neutral: The percentage of neutral emo sadness: The percentage of sadness emo surprise: The percentage of surprise

Orientation & Facial Accessories glass: The person is wearing glasses or not or sunglasses head pitch: The orientation of head(basically Up or down) head roll: The orientation of head(side ways rolling; horizontal or vertical) head yaw: The orientation of head(side facing; left or right) mouth close: The percentage of closed mouth mouth mask: The percentage of masked mouth mouth open: The percentage of open mouth mouth other: The percentage of other mouth things skin acne: The percentage of skin tone skin dark_circle: The percentage of dark circle on skin skin health: The growth of the skin percentage skin stain: The stain percentage on skin smile: The smile percentage

Region Columns nationality: The nationality belonging Followed by the percentage of each:- african celtic english
east asian
european
greek
hispanic
jewish
muslim
nordic
south asian

face_quality: The quality of the face recognized.

Acknowledgements

We wouldn't be here without the help of Kagglers. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Always wanted to contribute to the data science community and open up to questions.

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Here's the schema of Consumer Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_description
company_sic_code4 company_sic_code4_description
company_sic_code6
company_sic_code6_description
company_sic_code8
company_sic_code8_description company_parent_company
company_parent_company_location company_public_private company_subsidiary_company company_residential_business_code company_revenue_at_side_code company_revenue_range
company_revenue company_sales_volume
company_small_business company_stock_ticker company_year_founded company_minorityowned
company_female_owned_or_operated company_franchise_code company_dma company_dma_name
company_hq_address
company_hq_city company_hq_duns company_hq_state
company_hq_zip5 company_hq_zip4 co...

Updated 30 January 2023

Version 14 of Dataset

License Update:

There has been some confusion around licensing for this data set. Dr. Carla Patalano and Dr. Rich Huebner are the original authors of this dataset.

We provide a license to anyone who wishes to use this dataset for learning or teaching. For the purposes of sharing, please follow this license:

CC-BY-NC-ND This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Codebook

https://rpubs.com/rhuebner/hrd_cb_v14

PLEASE NOTE -- I recently updated the codebook - please use the above link. A few minor discrepancies were identified between the codebook and the dataset. Please feel free to contact me through LinkedIn (www.linkedin.com/in/RichHuebner) to report discrepancies and make requests.

Context

HR data can be hard to come by, and HR professionals generally lag behind with respect to analytics and data visualization competency. Thus, Dr. Carla Patalano and I set out to create our own HR-related dataset, which is used in one of our graduate MSHRM courses called HR Metrics and Analytics, at New England College of Business. We created this data set ourselves. We use the data set to teach HR students how to use and analyze the data in Tableau Desktop - a data visualization tool that's easy to learn.

This version provides a variety of features that are useful for both data visualization AND creating machine learning / predictive analytics models. We are working on expanding the data set even further by generating even more records and a few additional features. We will be keeping this as one file/one data set for now. There is a possibility of creating a second file perhaps down the road where you can join the files together to practice SQL/joins, etc.

Note that this dataset isn't perfect. By design, there are some issues that are present. It is primarily designed as a teaching data set - to teach human resources professionals how to work with data and analytics.

Content

We have reduced the complexity of the dataset down to a single data file (v14). The CSV revolves around a fictitious company and the core data set contains names, DOBs, age, gender, marital status, date of hire, reasons for termination, department, whether they are active or terminated, position title, pay rate, manager name, and performance score.

Recent additions to the data include: - Absences - Most Recent Performance Review Date - Employee Engagement Score

Acknowledgements

Dr. Carla Patalano provided the baseline idea for creating this synthetic data set, which has been used now by over 200 Human Resource Management students at the college. Students in the course learn data visualization techniques with Tableau Desktop and use this data set to complete a series of assignments.

Inspiration

We've included some open-ended questions that you can explore and try to address through creating Tableau visualizations, or R or Python analyses. Good luck and enjoy the learning!

• Is there any relationship between who a person works for and their performance score?
• What is the overall diversity profile of the organization?
• What are our best recruiting sources if we want to ensure a diverse organization?
• Can we predict who is going to terminate and who isn't? What level of accuracy can we achieve on this?
• Are there areas of the company where pay is not equitable?

There are so many other interesting questions that could be addressed through this interesting data set. Dr. Patalano and I look forward to seeing what we can come up with.

If you have any questions or comments about the dataset, please do not hesitate to reach out to me on LinkedIn: http://www.linkedin.com/in/RichHuebner

You can also reach me via email at: Richard.Huebner@go.cambridgecollege.edu

In the diverse and evolving field of Human Resources, effective communication and targeted outreach are crucial. Solution Publishing by Allforce stands as a pioneering digital audience data tool, specifically designed to connect B2B marketers with over 2.4 million HR professionals working in Benefits, Payroll, Recruiting, Training, and more, across 475,000 companies.

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This vast network encompasses a wide range of HR specialties, allowing you to tailor your marketing efforts to specific segments like Benefits, Payroll, Recruiting, or Training. This targeted approach ensures that your message reaches the most relevant professionals, enhancing engagement and response rates.

Direct Email Marketing: Connecting with HR Decision-Makers

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Enhance your LinkedIn marketing efforts with HR Continuum. By matching our rich data with LinkedIn profiles, your team can engage HR professionals with precision. Personalized InMail messages, strategic connection requests, and relevant content help build and nurture professional relationships in the HR community.

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Our data is not just a tool, but a gateway to the heart of the HR community. With our comprehensive database and multi-channel marketing approach, you are poised to effectively reach and influence key players in the HR field, driving impactful results for your B2B marketing efforts.

Social media platforms are integral to people's lives, offering ways to communicate, create and view content and share information. According to Ofcom, approximately 89% of UK internet users in 2023 used social media apps or sites. Teenagers and young adults are the biggest users, although there is rapid uptake among older age groups. Advertising is the primary revenue source for social media platforms, although subscription-based services are gaining momentum as platforms seek to diversify their incomes. TikTok is the success story of the last few years, becoming the most downloaded app between 2020 and 2022, according to Apptopia. The short-form video platform reported that it averaged revenue growth of over 450% between 2019 and 2022. After Musk's takeover, X, formerly known as Twitter, adjusted its content moderation and allowed previously banned accounts to return. As a result, over 600 advertisers have pulled their ads from the site because of fears their brand may be associated with malcontent. In response to falling ad revenue, X has introduced a subscription-based service which enables users to verify themselves and boosts the number of people who view their tweets. Meta-owned Facebook and Instagram have responded by introducing a similar service. Revenue is expected to grow by 14.3% in 2024-25, constrained by a slowdown in user growth for most major social media platforms. Over the five years through 2024-25, revenue is forecast to expand at a compound annual rate of 32.8% to reach £9.8 billion. Looking forward, regulations relating to how data is collected, stored, and shared will force advertisers and platforms to rethink how they can target their desired demographics. The rising prominence of AI will require the introduction of adequate regulations. The Online Safety Bill sets out new guidelines for social media platforms to abide by, with hefty fines in store for those who do not. Operating costs will swell as platforms look to meet consumers’ expectations, weighing on profit. Over the five years through 2029-30, social media platforms' revenue is projected to climb at an estimated 9.4% to reach £15.4 billion.

Economically Active Population Survey: Inactive population who have worked previously and left their last job more than 1 years ago by economic sector of last job, sex and age group. Percentages with regards the total in each age group. Annual. National.

McGRAW’s B2C to B2B Link 360 Masterfile offers an unprecedented connection between business and consumer data, combining over 80 million contacts with extensive demographic and professional data. This unique B2B-to-B2C overlay database, updated monthly, is designed to meet a range of B2B and B2C marketing, sales, and data science needs. It enables organizations to target business owners and executives, along with their corresponding consumer profiles, making it ideal for generating leads, segmenting audiences, and personalizing campaigns.

Key Features of McGRAW B2B and B2C Link 360 Masterfile

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1. Comprehensive Data Elements

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• Business Profile: Firmographics, industry classifications, revenue, employee count, and titles, providing a 360° view of target contacts.

1. Quantitative & Qualitative Insights

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2. Monthly Updates with High Fill Rates

   • Updated monthly, with 200-300K new company connections added regularly.
   • Maintains high fill rates, including 98% on business contact titles, 100% on 2-digit SIC industry codes, and extensive reach across 20 million business sites.

B2B Default Outputs: - Company Information: Name, Address, Industry, Revenue, Employee Count, Founded Year - Contact Information: Name, Email, Phone, Title - Geolocation Data: Latitude, Longitude, DMA, MSA information

B2C Default Outputs: - Consumer Demographics: Age, Income, Education, Ethnicity - Behavioral Insights: Interests, Vehicle Ownership, Homeownership - Financial Information: Income, Net Worth, Marital Status

Additional Outputs Available - Geographic & Address Details: Including SIC, NAICS codes, full address, HQ information - Personal & Professional Contacts: With LinkedIn, Twitter, Facebook profiles, personal identifiers - Enhanced Demographic Data: Race, Language, Religion, Occupation, Education Level

Additional Services

• Data Enhancement: Append consumer and business emails, LinkedIn profiles, and contact discovery to enrich existing records.
• Verification Services: Ensures data quality via call center-based business record validation.

McGRAW B2B and B2C Link 360 Masterfile database is a sophisticated, all-in-one data solution enabling businesses to enhance their outreach, improve targeting precision, and optimize their data resources for maximum impact across both B2B and B2C channels.

Success.ai's B2C Contact Data for European IT professionals is a game-changing solution for businesses aiming to connect with key decision-makers in the IT sector. Our platform offers access to over 170 million verified profiles, meticulously curated to include vital details such as work emails, phone numbers, and professional profiles. This dataset is ideal for businesses looking to fuel their B2B marketing, recruitment, and sales strategies with actionable insights and unparalleled accuracy.

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• Data Highlights 170M+ B2B Contact Profiles 50M Verified Phone Numbers 30M+ Company Profiles 70M Company LinkedIn Profiles 700M Global Professional Profiles

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The number of LinkedIn users in Australia was forecast to continuously increase between 2024 and 2028 by in total 0.5 million users (+3.74 percent). After the ninth consecutive increasing year, the LinkedIn user base is estimated to reach 13.89 million users and therefore a new peak in 2028. Notably, the number of LinkedIn users of was continuously increasing over the past years.User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 LinkedIn users in countries like Fiji and New Zealand.

Facebook is fast approaching 3 billion monthly active users. That’s about 36% of the world’s entire population that log in and use Facebook at least once a month.