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

Preliminary research efforts regarding Social Media Platforms and their contribution to website traffic in LAMs. Through the Similar Web API, the leading social networks (Facebook, Twitter, Youtube, Instagram, Reddit, Pinterest, LinkedIn) that drove traffic to each one of the 220 cases in our dataset were identified and analyzed in the first sheet. Aggregated results proved that Facebook platform was responsible for 46.1% of social traffic (second sheet).

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

Competitive Analysis of Industry Rivals The market for competitive analysis is expected to grow significantly over the forecast period, driven by increasing need for businesses to understand their competitive landscape. Key players in the market include BuiltWith, WooRank, SEMrush, Google, SpyFu, Owletter, SimilarWeb, Moz, SunTec Data, and TrendSource. These companies offer a range of services to help businesses track their competitors' online performance, including website traffic, social media engagement, and search engine rankings. Some of the key trends driving the growth of the market include the increasing adoption of digital marketing by businesses, the growing importance of social media, and the increasing availability of data and analytics tools. The market is segmented by type, application, and region. In terms of type, the market is divided into product analysis, traffic analytics, sales analytics, and others. In terms of application, the market is divided into SMEs and large enterprises. In terms of region, the market is divided into North America, South America, Europe, Middle East & Africa, and Asia Pacific. The North American region is expected to dominate the market during the forecast period, due to the presence of a large number of established players in the market. The Asia Pacific region is expected to grow at the highest CAGR during the forecast period, due to the increasing adoption of digital marketing by businesses in the region. This report provides a comprehensive analysis of the industry rivals, encompassing their concentration, product insights, regional trends, and key industry developments.

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

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

Start A New Notebook!

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

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.

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

The global market for website analytics and competitor analysis tools is experiencing robust growth, projected to reach $[Estimate based on available data, e.g., $5 billion] in 2025, with a Compound Annual Growth Rate (CAGR) of [Estimate, e.g., 12%] from 2025 to 2033. This expansion is driven by the increasing reliance of businesses, both large enterprises and SMEs, on data-driven decision-making for improved marketing strategies, website optimization, and competitive intelligence. Key trends shaping this market include the rising adoption of AI-powered analytics for deeper insights, the integration of website analytics with other marketing platforms, and the growing demand for comprehensive solutions that cover SEO, PPC, and social media analytics. While the market faces some restraints, such as the complexity of some analytics tools and the increasing cost of premium features, the overall growth trajectory remains positive. The competitive landscape is highly dynamic, with established players like Google, SEMrush, and SimilarWeb dominating the market through their comprehensive offerings and extensive user bases. However, smaller, specialized companies like BuiltWith, SpyFu, and WooRank are carving out niches for themselves by focusing on specific areas of website analytics or offering unique functionalities. The competitive intensity is driving innovation, leading to the development of more user-friendly interfaces, enhanced reporting capabilities, and improved data visualization tools. The market is also witnessing the emergence of new players offering innovative solutions leveraging cutting-edge technologies, promising further disruption and shaping the future of competitor analysis. Regional variations exist, with North America and Europe currently leading the market, but strong growth is expected from Asia-Pacific, particularly from countries like India and China, as digital adoption continues to accelerate.

The competitive marketing software market is experiencing robust growth, driven by the increasing need for businesses to understand their competitive landscape and optimize their marketing strategies. The market, estimated at $5 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% through 2033, reaching approximately $15 billion by the end of the forecast period. This growth is fueled by several key factors: the rising adoption of digital marketing, the increasing complexity of online competitive analysis, and the growing demand for data-driven marketing decisions. Key players like SEMrush, Ahrefs, and Moz Pro are leading this market, offering comprehensive suites of tools for keyword research, backlink analysis, competitor monitoring, and SEO optimization. The market's segmentation is likely diversified across various functionalities (e.g., SEO tools, social media analytics, PPC analysis) and business sizes, catering to both small and large enterprises. Growth is further boosted by ongoing technological advancements in data analytics and artificial intelligence, leading to more sophisticated and actionable insights for marketers. Despite its rapid expansion, the market faces challenges. High initial investment costs and the need for specialized technical expertise can act as barriers to entry for smaller businesses. Furthermore, the constant evolution of search engine algorithms and online marketing landscapes requires continuous software updates and adaptation from vendors. The market is also prone to intense competition, with established players constantly innovating and new entrants vying for market share. Nevertheless, the overall market outlook remains positive, with ongoing growth driven by the increasing reliance on data-driven decision-making and the evolving complexity of the digital marketing landscape. Regional variations in market penetration will likely exist, with North America and Europe expected to hold significant shares, followed by the Asia-Pacific region witnessing faster growth.

The competitor monitoring tool 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 $5 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $15 billion by 2033. This expansion is fueled by several key factors. The rise of digital marketing and the proliferation of online channels have intensified competition, making real-time competitor intelligence crucial for maintaining a competitive edge. Furthermore, the increasing adoption of cloud-based solutions offers scalability, accessibility, and cost-effectiveness, driving market penetration, particularly among SMEs. The market is segmented by application (SMEs and Large Enterprises) and type (Cloud-based and On-premises), with cloud-based solutions gaining significant traction due to their flexibility and ease of integration. North America currently holds the largest market share, followed by Europe and Asia Pacific, reflecting the higher adoption rates of advanced technologies in these regions. However, emerging markets in Asia Pacific and the Middle East & Africa are showing significant growth potential, presenting lucrative opportunities for market players. The market faces some restraints including the high initial investment costs for some advanced tools and the need for skilled personnel to effectively interpret and utilize the data generated. The competitive landscape is highly fragmented, with a mix of established players and emerging startups offering diverse solutions. Major players like SEMrush, Ahrefs, and SimilarWeb dominate the market with their comprehensive suite of tools. However, specialized players focusing on specific aspects of competitor monitoring, such as price tracking (Price2Spy, Prisync) or social media monitoring (Hootsuite, Sprout Social), are also gaining significant traction. The future of the market will likely see increased consolidation through mergers and acquisitions, along with the emergence of AI-powered solutions that offer more sophisticated analytics and predictive capabilities. This will further enhance the value proposition for businesses seeking to gain a competitive edge through effective competitor monitoring.

The Alternative Data Platform market is experiencing robust growth, driven by the increasing demand for non-traditional data sources within the financial services sector. The market's expansion is fueled by several key factors: the rise of quantitative investment strategies that heavily rely on alternative data for alpha generation; the growing sophistication of data analytics techniques capable of extracting meaningful insights from complex datasets; and the increasing availability of diverse alternative data streams, including social media sentiment, satellite imagery, and transactional data. This market is segmented across various data types (e.g., web traffic, social media, satellite imagery), industry verticals (e.g., finance, retail, healthcare), and deployment models (cloud-based, on-premise). The competitive landscape is characterized by both established players and emerging fintech companies, leading to ongoing innovation and consolidation. We estimate the market size in 2025 to be $5 billion, with a compound annual growth rate (CAGR) of 25% projected through 2033. This signifies substantial future opportunities for vendors and investors alike. Significant trends shaping this market include the increasing adoption of cloud-based platforms for scalability and cost-effectiveness, the rise of AI-powered data analytics for enhanced insight extraction, and a greater focus on data security and regulatory compliance. However, challenges remain. These include the high cost of alternative data acquisition and processing, the need for specialized expertise in data science and analytics, and concerns related to data quality and bias. Despite these restraints, the overall market outlook is positive, with continued growth driven by the expanding use of alternative data across a broader range of industries and investment strategies. The competitive landscape includes companies like Accelex, Exabel, Similarweb, Preqin, and many others actively innovating and expanding their offerings to meet the evolving needs of the market. This ongoing innovation and competition ensure a dynamic and rapidly changing marketplace.

Puff Bar, a disposable electronic nicotine delivery system (ENDS), was the ENDS brand most commonly used by U.S. youth in 2021. We explored whether Puff Bar’s rise in marketplace prominence was detectable through advertising, retail sales, social media, and web traffic data sources. We retrospectively documented potential signals of interest in and uptake of Puff Bar in the United States using metrics based on advertising (Numerator and Comperemedia), retail sales (NielsenIQ), social media (Twitter, via Sprinklr), and web traffic (Similarweb) data from January 2019 to June 2022. We selected metrics based on (1) data availability, (2) potential to graph metric longitudinally, and (3) variability in metric. We graphed metrics and assessed data patterns compared to data for Vuse, a comparator product, and in the context of regulatory events significant to Puff Bar. The number of Twitter posts that contained a Puff Bar term (social media), Puff Bar product sales measured in dollars (sales), and the number of visits to the Puff Bar website (web traffic) exhibited potential for surveilling Puff Bar due to ease of calculation, comprehensibility, and responsiveness to events. Advertising tracked through Numerator and Comperemedia did not appear to capture marketing from Puff Bar’s manufacturer or drive change in marketplace prominence. This study demonstrates how quantitative changes in metrics developed using advertising, retail sales, social media, and web traffic data sources detected changes in Puff Bar’s marketplace prominence. We conclude that low-effort, scalable, rapid signal detection capabilities can be an important part of a multi-component tobacco surveillance program.

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.