Traffic analytics, rankings, and competitive metrics for similarweb.com as of May 2025

Host country of organization for 86 websites in study.

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

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

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

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 ---

Comparison of definitions of total visits, unique visitors, bounce rate, and session duration conceptually and for the two analytics platforms: Google Analytics and SimilarWeb.

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

The global website analytics market, encompassing solutions for large enterprises and SMEs, is poised for significant growth. While the provided data lacks specific market size and CAGR figures, a reasonable estimation based on industry trends suggests a 2025 market size of approximately $15 billion, experiencing a compound annual growth rate (CAGR) of 12% from 2025 to 2033. This robust growth is fueled by several key drivers: the increasing reliance on data-driven decision-making across businesses, the escalating need for enhanced website performance optimization, and the growing adoption of sophisticated analytics tools offering deeper insights into user behavior and conversion rates. Market segmentation reveals strong demand across diverse analytics types, including product, traffic, and sales analytics. The competitive landscape is intensely dynamic, with established players like Google, SEMrush, and SimilarWeb vying for market share alongside emerging innovative companies like Owletter and TrendSource. These companies are constantly innovating to provide more comprehensive and user-friendly analytics platforms, leading to increased competition. This competitive pressure fosters innovation, but also necessitates strategic differentiation, focusing on specific niche markets or offering unique features to attract and retain customers. The market’s geographic distribution shows significant traction in North America and Europe, but emerging markets in Asia Pacific are also exhibiting substantial growth potential, driven by increasing internet penetration and digital transformation initiatives. While data security concerns and the complexity of implementing analytics tools present some restraints, the overall market outlook remains highly positive, promising considerable opportunities for market participants in the coming years.

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

Website type for the 86 websites in study.

Industry vertical of organization for 86 websites in study.

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.

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).

The Digital Ad Intelligence Software market is experiencing robust growth, driven by the increasing need for brands to optimize their advertising campaigns across diverse digital channels. This market, valued at approximately $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key factors, including the rising complexity of digital advertising landscapes, the demand for data-driven decision-making, and the proliferation of programmatic advertising. Businesses are increasingly relying on sophisticated software solutions to gain comprehensive insights into ad performance, competitor strategies, and audience behavior, leading to higher efficiency and return on investment. The market's segmentation encompasses various functionalities, including campaign tracking, competitor analysis, audience targeting optimization, and fraud detection. Key players like Pathmatics, SimilarWeb, and Sensor Tower are driving innovation through advanced analytics and AI-powered features, further consolidating the market's growth trajectory. The competitive landscape is characterized by a mix of established players and emerging startups, leading to continuous innovation and a diverse range of solutions. While the market enjoys significant growth potential, certain restraints exist, including the high cost of advanced software, data privacy concerns, and the need for specialized expertise to effectively utilize these tools. Despite these challenges, the overall outlook for the Digital Ad Intelligence Software market remains positive, with significant opportunities for growth in emerging markets and the continued adoption of advanced analytics capabilities. The forecast period of 2025-2033 presents substantial opportunities for both established vendors and new entrants to capitalize on the market's expanding potential and address the evolving needs of advertisers in an increasingly complex digital ecosystem. Further regional growth is expected, especially in Asia-Pacific and Latin America as digital advertising matures in these regions.

Among selected consumer electronics retailers worldwide, apple.com recorded the highest bounce rate in April 2024, at approximately 55.3 percent. Rival samsung.com had a slightly lower bounce rate of nearly 54 percent. Among selected consumer electronics e-tailers, huawei.com had the lowest bounce rate at 30.91 percent. Bounce rate is a marketing term used in web traffic analysis reflecting the percentage of visitors who enter the site and then leave without taking any further action like making a purchase or viewing other pages within the website ("bounce"). A sector with growth potential With one of the lowest online shopping cart abandonment rates globally in 2022, consumer electronics is a burgeoning e-commerce segment that places itself at the crossroads between technological progress and digital transformation. Boosted by the pandemic-induced surge in online shopping, the global market size of consumer electronics e-commerce was estimated at more than 340 billion U.S. dollars in 2021 and forecast to nearly double less than five years later. Amazon and Apple lead the charts in electronics e-commerce With more than 59 billion U.S. dollars in e-commerce net sales in the consumer electronics segment in 2022, apple.com was the uncontested industry leader. The global powerhouse surpassed e-commerce giants amazon.com and jd.com with more than ten billion U.S. dollars difference in online sales in the consumer electronics category.

The Alternative Data Platform market is experiencing robust growth, driven by the increasing demand for enhanced investment strategies and improved business decision-making across various sectors. The market's expansion is fueled by the rising availability of alternative data sources, including social media, satellite imagery, and transactional data, which offer unique insights unavailable through traditional methods. The shift towards cloud-based solutions is a significant trend, offering scalability, cost-effectiveness, and accessibility to a wider range of users. While the BFSI sector remains a key adopter, rapid adoption is also seen in the Retail and Logistics, and IT and Telecommunications sectors, driven by their need for real-time operational insights and predictive analytics. Competition is intense, with a mix of established players and innovative startups offering specialized platforms catering to diverse needs. However, challenges such as data security concerns, regulatory hurdles, and the need for sophisticated data analysis capabilities restrain widespread adoption. We estimate the 2025 market size at $5 billion, projecting a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, resulting in a substantial market size by 2033. This growth trajectory reflects the increasing recognition of alternative data's value in gaining a competitive edge. The market segmentation reveals a strong preference for cloud-based platforms due to their flexibility and scalability. North America currently holds the largest market share, benefiting from early adoption and a robust technology infrastructure. However, Asia Pacific is anticipated to show the highest growth rate over the forecast period, driven by increasing digitization and a burgeoning fintech sector. The sustained growth hinges on continued technological advancements, especially in AI and machine learning, which enhance data processing and analysis capabilities, leading to more refined insights and predictive models. Future market success will depend on vendors’ ability to address data security concerns through robust compliance measures and offer user-friendly interfaces that streamline data integration and interpretation for diverse user groups.

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.

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.

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.