General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

Analysis of ‘Popular Website Traffic Over Time ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/popular-website-traffice on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

Have you every been in a conversation and the question comes up, who uses Bing? This question comes up occasionally because people wonder if these sites have any views. For this research study, we are going to be exploring popular website traffic for many popular websites.

Methodology

The data collected originates from SimilarWeb.com.

Source

For the analysis and study, go to The Concept Center

This dataset was created by Chase Willden and contains around 0 samples along with 1/1/2017, Social Media, technical information and other features such as: - 12/1/2016 - 3/1/2017 - and more.

How to use this dataset

• Analyze 11/1/2016 in relation to 2/1/2017
• Study the influence of 4/1/2017 on 1/1/2017
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

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--- Original source retains full ownership of the source dataset ---

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address hostmaster@similarweb.com..

Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.

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.

Similarweb's Surge: A Sign of Digital Dominance? (SMWB)

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

Host country of organization for 86 websites in study.

The Alternative Data Market size was valued at USD 7.20 billion in 2023 and is projected to reach USD 126.50 billion by 2032, exhibiting a CAGR of 50.6 % during the forecasts period. The use and processing of information that is not in financial databases is known as the alternative data market. Such data involves posts in social networks, satellite images, credit card transactions, web traffic and many others. It is mostly used in financial field to make the investment decisions, managing risks and analyzing competitors, giving a more general view on market trends as well as consumers’ attitude. It has been found that there is increasing requirement for the obtaining of data from unconventional sources as firms strive to nose ahead in highly competitive markets. Some current trend are the finding of AI and machine learning to drive large sets of data and the broadening utilization of the so called “Alternative Data” across industries that are not only the finance industry. Recent developments include: In April 2023, Thinknum Alternative Data launched new data fields to its employee sentiment datasets for people analytics teams and investors to use this as an 'employee NPS' proxy, and support highly-rated employers set up interviews through employee referrals. , In September 2022, Thinknum Alternative Data announced its plan to combine data Similarweb, SensorTower, Thinknum, Caplight, and Pathmatics with Lagoon, a sophisticated infrastructure platform to deliver an alternative data source for investment research, due diligence, deal sourcing and origination, and post-acquisition strategies in private markets. , In May 2022, M Science LLC launched a consumer spending trends platform, providing daily, weekly, monthly, and semi-annual visibility into consumer behaviors and competitive benchmarking. The consumer spending platform provided real-time insights into consumer spending patterns for Australian brands and an unparalleled business performance analysis. .

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.

SMWB Similarweb Ltd. Ordinary 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

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.

SimilarWeb (SMWB) - Tracking Digital Trends: Will it Drive Growth?

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 dataset contains 4 parts. "SimilarWeb dataset with screenshots" is created by scraping web elements, their CSS, and corresponding screenshots in three different time intervals for around 100 web pages. Based on this data, the "SimilarWeb dataset with SSIM column" is created with the target column containing the structural similarity index measure (SSIM) of the captured screenshots. This part of the dataset is used to train machine learning regression models. To evaluate approach, "Accessible web pages dataset" and "General use web pages dataset" parts of the dataset are used.

The Alternative Data Platform market is experiencing robust growth, driven by the increasing need for businesses across diverse sectors to leverage non-traditional data sources for improved decision-making. The market, estimated at $5 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), fueled by a Compound Annual Growth Rate (CAGR) of 25%. This growth is primarily attributed to several key factors. Firstly, the rising adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting businesses of all sizes. Secondly, the expanding application of alternative data in areas like fraud detection (BFSI), supply chain optimization (Retail and Logistics), and market prediction (IT and Telecommunications) is pushing market expansion. Furthermore, the increasing availability and affordability of alternative data sources, combined with advancements in data analytics and machine learning, are enabling businesses to extract greater value from these non-traditional datasets. While data security and privacy concerns present a challenge, the overall market outlook remains overwhelmingly positive. The market segmentation reveals strong growth across various applications and types. The BFSI sector is a major driver due to its need for enhanced risk management and fraud prevention. The cloud-based segment dominates the market due to its flexibility and accessibility. North America currently holds the largest market share, followed by Europe and Asia Pacific, reflecting the higher level of technological advancement and adoption in these regions. However, the Asia Pacific region is poised for significant growth due to increasing digitalization and rising investments in data analytics infrastructure. The competitive landscape is dynamic, with a mix of established players and emerging startups offering diverse solutions. The success of individual companies depends on their ability to innovate, provide reliable data, ensure data security, and offer user-friendly platforms. Competition is likely to intensify as more companies enter this rapidly evolving market.

The Competitive Intelligence (CI) tools market is experiencing robust growth, driven by the increasing need for businesses of all sizes to understand their competitive landscape and make data-driven decisions. The market, estimated at $10 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% between 2025 and 2033, fueled by several key factors. The rise of digitalization and the proliferation of online data are creating a wealth of information that CI tools can effectively analyze and leverage. Furthermore, the growing adoption of cloud-based solutions is enhancing accessibility and scalability for businesses, contributing to market expansion. The market is segmented by company size (large enterprises and SMEs) and deployment type (on-premise and cloud-based), with cloud-based solutions gaining significant traction due to their cost-effectiveness and flexibility. Large companies are the primary adopters, but SMEs are increasingly recognizing the value proposition of CI tools for competitive advantage, contributing to broader market penetration. Geographic expansion is also driving growth, with North America and Europe currently holding the largest market share, although the Asia-Pacific region is expected to witness significant growth in the coming years driven by rapid technological advancements and increasing business activity. Despite the positive outlook, the market faces certain challenges. High implementation and maintenance costs, coupled with the need for specialized expertise, can be barriers to entry for some smaller businesses. The complexity of analyzing the vast amount of data generated also requires sophisticated tools and well-trained personnel. Moreover, the constantly evolving competitive landscape and the need for continuous data updates present ongoing challenges for both vendors and users. However, technological advancements, such as the integration of artificial intelligence and machine learning in CI tools, are helping to address these challenges by improving the efficiency and accuracy of data analysis. This ongoing innovation, along with growing awareness of the strategic importance of CI, will continue to propel market growth in the forecast period.

Website type for the 86 websites in study.

Industry vertical of organization for 86 websites in study.

The market for competitor analysis tools is experiencing robust growth, driven by the increasing importance of competitive intelligence in today's dynamic business landscape. The surge in digital marketing and the need for businesses, both SMEs and large enterprises, to understand their competitive positioning fuels demand for sophisticated tools offering comprehensive data analysis and actionable insights. Cloud-based solutions are dominating the market due to their scalability, accessibility, and cost-effectiveness compared to on-premises deployments. Key players like SEMrush, Ahrefs, and SimilarWeb are establishing strong market presence through continuous innovation, comprehensive feature sets, and targeted marketing strategies. However, the market also faces challenges, including the rising costs of data acquisition and the complexity of integrating various tools into existing workflows. The competitive landscape is characterized by a mix of established players and emerging niche providers. Differentiation is achieved through unique data sources, specialized analytics capabilities, and the ability to integrate seamlessly with other marketing and business intelligence platforms. The North American and European markets currently hold a significant share, owing to high technology adoption and established digital marketing ecosystems. However, growth is expected in Asia-Pacific regions as businesses in developing economies increasingly adopt digital strategies and seek competitive advantages. The forecast period (2025-2033) suggests continued expansion, propelled by technological advancements like AI-powered insights and the expanding use of social media analytics within competitor analysis. The market's segmentation reflects varying needs across different business sizes and deployment preferences. While large enterprises typically opt for comprehensive, feature-rich solutions capable of handling large datasets and integrating with various systems, SMEs often prioritize cost-effective, user-friendly tools providing essential insights. The choice between cloud-based and on-premises solutions depends on factors like IT infrastructure, security considerations, and budget constraints. As the market matures, we anticipate further consolidation through mergers and acquisitions, and the emergence of more specialized tools catering to specific industry needs. The overall trajectory indicates continued strong growth, with a focus on enhanced data analysis, improved user experiences, and seamless integration within broader business intelligence platforms.

Comparison of user, site, and network-centric approaches to web analytics data collection showing advantages, disadvantages, and examples of each approach at the time of the study.

Market Size and Drivers: The global Customer Behavior Analysis Tool market is expected to reach a value of USD 12.5 billion by 2033, growing at a CAGR of 12.3% from 2025 to 2033. Rapidly evolving customer behavior, the surge in e-commerce, and the need for personalized marketing experiences are key drivers of market growth. The growing adoption of cloud-based solutions and the advancements in AI and machine learning technologies are further fueling market expansion. Competitive Landscape and Regional Distribution: The market landscape is highly competitive, with established players such as Similarweb, Google, and Facebook leading the pack. Other notable players include Zoho, Kissmetrics, Brand24, Brandwatch, Woopra, Mixpanel, Hotjar, Smartlook, HubSpot, Trifacta, Crazyegg, Sprout Social, Amplitude, Heap, FullStory, Tableau, Segment, Vertica, VWO, Userpilot, SAP, Teradata, Oracle, Salesforce, and Manthan System. North America holds the largest market share due to the presence of major technology hubs and early adoption of advanced analytics tools. Asia Pacific is expected to witness significant growth during the forecast period, primarily driven by rising digital penetration and the growth of e-commerce in the region.

In the six months ending March 2024, the United States accounted for over 45 percent of the web visits to Tumblr.com. Ranked second, the United Kingdom accounted for 5.74 percent of traffic to the social networking website, followed by Canada, which accounted for 5.01 percent of the web page online volume.

In March 2024, the social network Tumblr.com had 191.1 million website visits worldwide, down from 198.3 million site visits by the end of 2023. In December 2018, the platform banned porn and adult content, a controversial move that sparked user outrage and caused artists and sex workers to move to other sites.