This statistic displays the results of survey on the share of real estate firms using social media in Italy in 2019, by social media usage. During the survey period it was found that 41.8 percent of the responding companies used at least one social media platform.

The real estate marketing services market is experiencing robust growth, driven by increasing adoption of digital marketing strategies and a competitive landscape demanding innovative approaches to reach potential buyers and sellers. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant growth is fueled by several key factors. Firstly, the rising popularity of online property portals and social media platforms necessitates sophisticated digital marketing strategies. Secondly, the increasing use of data analytics and targeted advertising allows real estate professionals to reach specific demographics with tailored campaigns. Finally, the ongoing demand for reputation management services highlights the importance of building and maintaining a strong online presence. Segmentation analysis reveals that Media Advertising, Search Engine Optimization (SEO), and Reputation Management constitute major service types within the market. Application-wise, Realtors, Brokers, and Developers represent significant consumer segments. The competitive landscape comprises both established marketing agencies specializing in real estate and smaller, niche providers leveraging their individual expertise. This dynamic environment encourages continuous innovation in marketing tactics and fuels market expansion. This growth is not uniform across all segments. While SEO and reputation management maintain steady high demand, the media advertising segment shows a trend towards specialized, targeted campaigns rather than blanket advertising. The geographical distribution of the market reflects global trends in real estate activity, with North America and Europe currently dominating the market share, driven by robust economies and advanced digital infrastructure. However, Asia-Pacific and other emerging markets are expected to witness significant growth in the coming years due to expanding middle classes and increasing real estate investments. The market's evolution necessitates real estate professionals to adapt to changing consumer behaviour and adopt innovative strategies, fostering a constant pursuit of improved marketing techniques and technologies. This dynamic interplay between technological advancements, evolving consumer preferences, and competitive pressures will shape the future trajectory of the real estate marketing services market.

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

This dataset was generated for analyzing the economic impacts of subway networks on housing prices in metropolitan areas. The provision of transit networks and accompanying improvement in accessibility induce various impacts and we focused on the economic impacts realized through housing prices. As a proxy of housing price, we consider the price of condominiums, the dominant housing type in South Korea. Although our focus is transit accessibility and housing prices, the presented dataset is applicable to other studies. In particular, it provides a wide range of variables closely related to housing price, including housing properties, local amenities, local demographic characteristics, and control variables for the seasonality. Many of these variables were scientifically generated by our research team. Various distance variables were constructed in a geographic information system environment based on public data and they are useful not only for exploring environmental impacts on housing prices, but also for other statistical analyses in regard to real estate and social science research. The four metropolitan areas covered by the data—Busan, Daegu, Daejeon, and Gwangju—are independent of the transit systems of Greater Seoul, providing accurate information on the metropolitan structure separate from the capital city.

Point-of-interest (POI) is defined as a physical entity (such as a business) in a geo location (point) which may be (of interest).

We strive to provide the most accurate, complete and up to date point of interest datasets for all countries of the world. The United Arab Emirates POI Dataset is one of our worldwide POI datasets with over 98% coverage.

This is our process flow:

Our machine learning systems continuously crawl for new POI data
Our geoparsing and geocoding calculates their geo locations
Our categorization systems cleanup and standardize the datasets
Our data pipeline API publishes the datasets on our data store

POI Data is in a constant flux - especially so during times of drastic change such as the Covid-19 pandemic.

Every minute worldwide on an average day over 200 businesses will move, over 600 new businesses will open their doors and over 400 businesses will cease to exist.

In today's interconnected world, of the approximately 200 million POIs worldwide, over 94% have a public online presence. As a new POI comes into existence its information will appear very quickly in location based social networks (LBSNs), other social media, pictures, websites, blogs, press releases. Soon after that, our state-of-the-art POI Information retrieval system will pick it up.

We offer our customers perpetual data licenses for any dataset representing this ever changing information, downloaded at any given point in time. This makes our company's licensing model unique in the current Data as a Service - DaaS Industry. Our customers don't have to delete our data after the expiration of a certain "Term", regardless of whether the data was purchased as a one time snapshot, or via a recurring payment plan on our data update pipeline.

The main differentiators between us vs the competition are our flexible licensing terms and our data freshness.

The core attribute coverage is as follows:

Poi Field Data Coverage (%) poi_name 100 brand 4 poi_tel 48 formatted_address 100 main_category 96 latitude 100 longitude 100 neighborhood 2 source_url 47 email 6 opening_hours 43

The data may be visualized on a map at https://store.poidata.xyz/ae and a data sample may be downloaded at https://store.poidata.xyz/datafiles/ae_sample.csv

https://store.poidata.xyz/in Point-of-interest (POI) is defined as a physical entity (such as a business) in a geo location (point) which may be (of interest).

We strive to provide the most accurate, complete and up to date point of interest datasets for all countries of the world. The India POI Dataset is one of our worldwide POI datasets.

This is our process flow:

Our machine learning systems continuously crawl for new POI data
Our geoparsing and geocoding calculates their geo locations
Our categorization systems cleanup and standardize the datasets
Our data pipeline API publishes the datasets on our data store

POI Data is in a constant flux - especially so during times of drastic change such as the Covid-19 pandemic.

Every minute worldwide on an average day over 200 businesses will move, over 600 new businesses will open their doors and over 400 businesses will cease to exist.

In today's interconnected world, of the approximately 200 million POIs worldwide, over 94% have a public online presence. As a new POI comes into existence its information will appear very quickly in location based social networks (LBSNs), other social media, pictures, websites, blogs, press releases. Soon after that, our state-of-the-art POI Information retrieval system will pick it up.

We offer our customers perpetual data licenses for any dataset representing this ever changing information, downloaded at any given point in time. This makes our company's licensing model unique in the current Data as a Service - DaaS Industry. Our customers don't have to delete our data after the expiration of a certain "Term", regardless of whether the data was purchased as a one time snapshot, or via a recurring payment plan on our data update pipeline.

The main differentiators between us vs the competition are our flexible licensing terms and our data freshness.

The core attribute coverage for India is as follows: Poi Field Data Coverage (%) poi_name 100 brand 3 poi_tel 17 formatted_address 100 main_category 100 latitude 100 longitude 100 neighborhood 7 source_url 24 email 2 opening_hours 26

The dataset may be viewed online at https://store.poidata.xyz/in and a data sample may be downloaded at https://store.poidata.xyz/datafiles/in_sample.csv

J. Supor & Son is a transportation and logistics company that provides a range of services including heavy hauling, trucking, railcar transportation, crane and rigging, and warehousing. With over 50 years of experience, the company has built a reputation for providing excellent service and expertise to its customers. Its commitment to innovation and technology has allowed it to stay ahead of the curve, with a fleet of advanced vehicles, specialist equipment, and state-of-the-art storage facilities.

Throughout its history, J. Supor & Son has demonstrated its ability to handle complex and specialized projects, including catastrophic recovery and turn-key projects. The company's extensive network of logistics and transportation solutions makes it a one-stop-shop for clients, with a focus on providing personalized solutions to meet their unique needs. With a strong presence on social media and a commitment to customer service, J. Supor & Son is a reliable and trusted partner for businesses and individuals alike.

Snapshot img

Visual Content Market Size 2025-2029

The visual content market size is forecast to increase by USD 1.24 billion at a CAGR of 5.1% between 2024 and 2029.

The market, encompassing digital stock images and software-generated graphics, continues to experience significant growth In the US. Key drivers include the increasing demand for digital content in various sectors such as real estate, education, and digital marketing. A catalyst for this growth is the rising preference for visuals like 360-degree images and videos. However, the market faces challenges, including limited online video consumption due to slow internet speeds. As digital marketing becomes more prevalent, the need for high-quality, visually engaging content is increasingly important. This trend is expected to continue, with advancements in technology further enhancing the potential of visual content to captivate audiences and drive engagement.

What will be the Size of the Visual Content Market During the Forecast Period?

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The market continues to expand as businesses recognize the power of engaging, shareable content to capture audience attention and drive performance. The human brain processes visual information 60,000 times faster than text, making infographics, videos, photos, and interactive visuals effective tools for conveying complex information and boosting brand awareness. For example, a brand may include a CTA in an infographic, inviting users to sign up for a newsletter or download an e-book. Visual content drives ROI through increased traffic, backlinks, and calls to action. 



Platforms and others provide businesses with a range of image-based and interactive content solutions. As the market evolves, expect to see a continued focus on creating high-quality, shareable visuals that resonate with audiences and deliver measurable results. Visual capitalists are leveraging a variety of formats, including pictures, diagrams, charts, online videos, slide decks, native video, and ultimate guides, to present complex data and insights in an engaging and accessible way.

How is this Visual Content Industry segmented and which is the largest segment?

The 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.

Product

  Stock images
  Stock video


Application

  Editorial
  Commercial


License Model

  RF
  RM


End-user

  Media and entertainment
  Advertising
  Corporate
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    India
    Japan


  Middle East and Africa



  South America

By Product Insights

The stock images segment is estimated to witness significant growth during the forecast period.

The market experienced significant growth in 2024, with stock images leading the segment. The proliferation of digital photography, driven by the easy accessibility and affordability of digital single-lens reflex (DSLR) cameras, has contributed to market expansion. Notably, there has been an increasing trend of collaborations among companies, enabling them to broaden their offerings, reach larger audiences, and enhance customer value. The market exhibits minimal price differentiation based on picture resolution due to the transition to mobile and online platforms. The demand for responsive web design has fueled the need for high-quality, small images, leading to advancements in image resolution technology. Visual content encompasses various formats, including infographics, videos, YouTube, Hubspot, and social media, among others.

Get a glance at the Visual Content Industry report of share of various segments Request Free Sample

The stock images segment was valued at USD 3.38 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 38% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The North American market holds the largest share In the global visual content industry. The US is the primary contributor to this market's growth due to the increasing demand for video content among commercial consumers. Factors such as enhanced broadband penetration and faster internet speeds facilitate smoother video consumption. Furthermore, the proliferation of social media platforms like Facebook and Instagram In the US fuels market expansion. Visual content encompasses various formats, including infographics, videos, YouTube, Hubspot, and interactive visuals. These ele

The virtual goods market, a dynamic and rapidly expanding segment of the digital economy, encompasses a wide range of non-tangible products including avatars, skins, in-game currency, and virtual real estate. Virtual goods are primarily used within online games, social media platforms, and virtual reality environmen

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

PEI^D Pennsylvania Real Estate Investment Trust 6.875% Series D Cumulative Redeemable Perpetual Preferred Shares

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Point-of-interest (POI) is defined as a physical entity (such as a business) in a geo location (point) which may be (of interest).

We strive to provide the most accurate, complete and up to date point of interest datasets for all countries of the world. The South Africa POI Dataset is one of our worldwide POI datasets with over 98% coverage.

This is our process flow:

Our machine learning systems continuously crawl for new POI data
Our geoparsing and geocoding calculates their geo locations
Our categorization systems cleanup and standardize the datasets
Our data pipeline API publishes the datasets on our data store

POI Data is in a constant flux - especially so during times of drastic change such as the Covid-19 pandemic.

Every minute worldwide on an average day over 200 businesses will move, over 600 new businesses will open their doors and over 400 businesses will cease to exist.

In today's interconnected world, of the approximately 200 million POIs worldwide, over 94% have a public online presence. As a new POI comes into existence its information will appear very quickly in location based social networks (LBSNs), other social media, pictures, websites, blogs, press releases. Soon after that, our state-of-the-art POI Information retrieval system will pick it up.

We offer our customers perpetual data licenses for any dataset representing this ever changing information, downloaded at any given point in time. This makes our company's licensing model unique in the current Data as a Service - DaaS Industry. Our customers don't have to delete our data after the expiration of a certain "Term", regardless of whether the data was purchased as a one time snapshot, or via a recurring payment plan on our data update pipeline.

The main differentiators between us vs the competition are our flexible licensing terms and our data freshness.

The core attribute coverage is as follows:

Poi Field Data Coverage (%) poi_name 100 brand 8 poi_tel 67 formatted_address 100 main_category 98 latitude 100 longitude 100 neighborhood 1 source_url 43 email 8 opening_hours 47

The data may be visualized on a map at https://store.poidata.xyz/za and a data sample may be downloaded at https://store.poidata.xyz/datafiles/za_sample.csv

Weibo Data Description:Tweets of Weibo during the Spring Festival travel rush, from January 13 to February 21 in 2017 are thoroughly collected to establish the migration network of workforce at the granularity of city.Basic Statistics:Number of cities: 371Number of city pairs(directed): 120,361Number of city pairs(undirected): 61,759Number of all flux: 41,454,268 Data Format:Each line in the file(WorkforceMigrate.csv) demonstrates a 12-tuple (city1,city2,flux,gdp1,gdp2,ave_gdp1,ave_gdp2,population1,population2,geographical_distance,travel_time,travel_distance) defined to denote workforce movement from city1 to city2. Details can be found as follows.1. city1: origin city id2. city2: destination city id3. flux: number of movements from city1 to city24. gdp1: GDP of city15. gdp2: GDP of city26. avg_gdp1: the per capita GDP of city17. avg_gdp2: the per capita GDP of city28. population1: the number of permanent residents in city19. population2: the number of permanent residents in city210. geographical_distance: geographical distance between city1 and city211. travel_distance: travel distance from city1 to city2 provided by Baidu Map API12. travel_time: travel time from city1 to city2 provided by Baidu Map APIData Description:The demographic and economic information in 2015 are collected at the granularity of province.Province Data Format:Each line in file(ProvinceInfo.csv) demonstrates a 8-tuple (province, gdp15, Information Technology Industry,Financial Industry,Real Estate Industry,Scientific Research and Technical Services Industry,income15,R&D) defined to denote the economci information of provinces. Details can be found as follows:1. province: province id2. gdp15: GDP of province3. Information Technology Industry: ratio of practitioner in the information technology industry4. Financial Industry: ratio of practitioner in the Financial industry5. Real Estate Industry: ratio of practitioner in the real estate industry 6. Scientific Research and Technical Services Industry: ratio of practitioner in the scientific research and technical services industry 7. income15: per capita disposable income8. R&D: the fund investment for research and developmentTrain Data Desciption:The national railway line data, including 5,878 trains in total from train schedule are collected to establish the train network at the granularity of city.Basic Statistics:Number of cities: 284 citiesNumber of citi pairs(undirected): 12381Data Format:Each line in the file(Train.csv) demonstrate a triple (city1, city2, train_count) defined to denote trains that pass through city1 and city2. Details can be found as follows:1. city1: city id2. city2: city id3. train_count: the number of trains that pass through city1 and city2Any issues please feel free to contact jichang@buaa.edu.cn.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Centerspace (CSR) Stock Forecast: Navigating the Real Estate Market

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The online classified market size was valued at USD 131.31 billion in 2024 and is set to exceed USD 2.08 trillion by 2037, registering over 23.7% CAGR during the forecast period i.e., between 2025-2037. North America industry is estimated to hold largest revenue share by 2037, backed by rising awareness for online classified advertisements, along with increasing number of internet users in the region.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

LND Brasilagro Brazilian Agric Real Estate Co Sponsored ADR (Brazil)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This table contains data on the use of information and communication technologies (ICT) by companies.

These include: — the use of computers; — access to and use of (mobile) internet; — software and its application; — Supply chain management (chain integration); —Automatic Data Exchange; — communication with governments via the Internet; — the extent to which companies use the internet for buying and selling; — innovative activities;

the use of social media. The data are broken down by industry. The data relate to companies with 10 and more employees. The research population consists of: C Industry D Energy supply E Water companies and waste management F Construction industry G Trade H Transport and storage I Horeca J Information and communication K Financial services (sbi 2008 codes 64.19, 64.92, 65.1-65.2 and 66.12-66.19) L Real estate rental and trading M Specialist business services N Rental and other business services Q Health and welfare care The reporting period for this table is January 2012.However, a number of topics relate to the year 2011. If this is the case, this is stated in the explanatory memorandum to the subject.The figures in this table are from the survey ‘ICT use companies 2011’. A number of topics were asked about the situation in 2011 and a number of the situation in January 2012. It was chosen to use 2012 in the title of this table. The previous survey asked for the situation in 2010 for all topics. The results can be found in the table ‘Industrial use in enterprises, 2010’. Due to this change in the reporting period of the investigation, no table is available with ‘2011’ in the title. Data available for 2012.

Status of the figures: The figures in this table are final.

Changes as of 8 March 2019: None, table has been discontinued.

When will there be new figures? No longer applicable.This table contains data on the use of information and communication technologies (ICT) by companies.

These include:

— the use of computers; — access to and use of (mobile) internet; — software and its application; — Supply chain management (chain integration); —Automatic Data Exchange; — communication with governments via the Internet;

— the extent to which companies use the internet for buying and selling; — innovative activities; the use of social media.

The data are broken down by industry. The data relate to companies with 10 and more employees. The research population consists of: C Industry D Energy supply E Water companies and waste management F Construction industry

G Trade H Transport and storage I Horeca J Information and communication K Financial services (sbi 2008 codes 64.19, 64.92, 65.1-65.2 and 66.12-66.19) L Real estate rental and trading M Specialist business services

N Rental and other business services Q Health and welfare care

The reporting period for this table is January 2012. However, a number of topics relate to the year 2011. If this is the case, this is stated in the explanatory memorandum to the subject.The figures in this table are from the survey ‘ICT use companies 2011’. A number of topics were asked about the situation in 2011 and a number of the situation in January 2012. It was chosen to use 2012 in the title of this table. The previous survey asked for the situation in 2010 for all topics. The results can be found in the table ‘Industrial use in enterprises, 2010’.Due to this change in the reporting period of the investigation, no table is available with ‘2011’ in the title.

Data available for 2012.

Status of the figures: The figures in this table are final.

Changes as of 8 March 2019: None, table has been discontinued.

When will there be new figures?

No longer applicable. This table contains data on the use of information and communication technologies (ICT) by companies. These include: — the use of computers; — access to and use of (mobile) internet; — software and its application; — Supply chain management (chain integration); —Automatic Data Exchange; — communication with governments via the Internet; — the extent to which companies use the internet for buying and selling; — innovative activities; the use of social media.

The data are broken down by industry. The data relate to companies with 10 and more employees. The research population consists of: C Industry

D Energy supply E Water companies and waste management F Construction industry G Trade H Transport and storage I Horeca J Information and communication K Financial services (sbi 2008 codes 64.19, 64.92, 65.1-65.2 and 66.12-66.19) L Real estate rental and trading M Specialist business services N Rental and other business services Q Health and welfare care The reporting period for this table is January 2012. However, a number of topics relate to the year 2011. If this is the case, this is stated in the explanatory memorandum to the subject. The figures in this table are from the survey ‘ICT use companies 2011’. A number of topics were asked about the situation in 2011

The use of social media by Portuguese enterprises, in 2023, was mostly undertaken by those in the construction and real estate sector. Indeed, 100 percent of these enterprises used social media during the period in question. Following were companies from the information and communication and also catering and accommodation sectors, at a share of 99.5 percent. Enterprises from the transportation and storage were the ones using social media the least, at a rate of 96.7 percent.

ExactOne delivers unparalleled consumer transaction insights to help investors and corporate clients uncover market opportunities, analyze trends, and drive better decisions.

Dataset Highlights - Source: Debit and credit card transactions from 600K+ active users and 2M accounts connected via Open Banking. Scale: Covers 250M+ annual transactions, mapped to 1,800+ merchants and 330+ tickers. Historical Depth: Over 6 years of transaction data. Flexibility: Analyse transactions by merchant/ticker, category/industry, or timeframe (daily, weekly, monthly, or quarterly).

ExactOne data offers visibility into key consumer industries, including: Airlines - Regional / Budget Airlines - Cargo Airlines - Full Service Autos - OEMs Communication Services - Cable & Satellite Communication Services - Integrated Telecommunications Communication Services - Wireless Telecom Consumer - Services Consumer - Health & Fitness Consumer Staples - Household Supplies Energy - Utilities Energy - Integrated Oil & Gas Financial Services - Insurance Grocers - Traditional Hotels - C-corp Industrial - Tools And Hardware Internet - E-commerce Internet - B2B Services Internet - Ride Hailing & Delivery Leisure - Online Gambling Media - Digital Subscription Real Estate - Brokerage Restaurants - Quick Service Restaurants - Fast Casual Restaurants - Pubs Restaurants - Specialty Retail - Softlines Retail - Mass Merchants Retail - European Luxury Retail - Specialty Retail - Sports & Athletics Retail - Footwear Retail - Dept Stores Retail - Luxury Retail - Convenience Stores Retail - Hardlines Technology - Enterprise Software Technology - Electronics & Appliances Technology - Computer Hardware Utilities - Water Utilities

Use Cases

For Private Equity & Venture Capital Firms: - Deal Sourcing: Identify high-growth opportunities. - Due Diligence: Leverage transaction data to evaluate investment potential. - Portfolio Monitoring: Track performance post-investment with real-time data.

For Consumer Insights & Strategy Teams: - Market Dynamics: Compare sales trends, average transaction size, and customer loyalty. - Competitive Analysis: Benchmark market share and identify emerging competitors. - E-commerce vs. Brick & Mortar Trends: Assess channel performance and strategic opportunities. - Demographic & Geographic Insights: Uncover growth drivers by demo and geo segments.

For Investor Relations Teams: - Shareholder Insights: Monitor brand performance relative to competitors. - Real-Time Intelligence: Analyse sales and market dynamics for public and private companies. - M&A Opportunities: Evaluate market share and growth potential for strategic investments.

Key Benefits of ExactOne - Understand Market Share: Benchmark against competitors and uncover emerging players. - Analyse Customer Loyalty: Evaluate repeat purchase behavior and retention rates. - Track Growth Trends: Identify key drivers of sales by geography, demographic, and channel. - Granular Insights: Drill into transaction-level data or aggregated summaries for in-depth analysis.

With ExactOne, investors and corporate leaders gain actionable, real-time insights into consumer behaviour and market dynamics, enabling smarter decisions and sustained growth.