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

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

The dataset used in the paper

Host country of organization for 86 websites in study.

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

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

Website type for the 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.

Industry vertical of organization for 86 websites in study.

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

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) Stock Price Outlook Sees Shift

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 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.

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, thegioididong.com recorded the highest bounce rate in July 2025, at approximately ***** percent. apple.com had a slightly lower bounce rate of nearly ***** percent. Among selected consumer electronics e-tailers, sony.com had the lowest bounce rate at ***** 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 *** 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 ** 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 *** billion U.S. dollars difference in online sales in the consumer electronics category.

Определение: Общий трафик на 15 сайтов с искусственным интеллектом со стационарных и мобильных компьютеров в каждой стране. [Переведено с en: английского языка] Тематическая область: Информационно-коммуникационные технологии [Переведено с en: английского языка] Область применения: Искусственный интеллект [Переведено с en: английского языка] Единица измерения: Количество посещений [Переведено с en: английского языка] Примечание: Similarweb не предоставляет точных данных о количестве посещений веб-сайтов, которые посещают менее 5000 человек. В этих случаях используется приблизительная оценка в 4999 посещений. [Переведено с es: испанского языка] Источник данных: Цифровая обсерватория Десарролло (ODD) на основе Similarweb [Переведено с es: испанского языка] Последнее обновление: Feb 9 2024 1:04PM Организация-источник: Экономическая комиссия по Латинской Америке и Карибскому бассейну [Переведено с en: английского языка] Definition: Total traffic to 15 artificial intelligence sites from fixed and mobile computers per country. Thematic Area: Information and Communication Technologies Application Area: Artificial intelligence Unit of Measurement: Number of visits Note: Similarweb does not provide an exact number of visits for websites that receive fewer than 5,000 visits. In these cases, an approximate estimate of 4,999 is used. Data Source: Observatorio de Desarrollo Digital (ODD) based on Similarweb Last Update: Feb 9 2024 1:04PM Source Organization: Economic Comission for Latin America and the Caribbean

Notice: You can check the new version 0.9.6 at the official page of Information Management Lab and at the Google Data Studio as well.

Description of the Report and Topic Justification

Now that the ICTs have matured, Information Organizations such as Libraries, Archives and Museums, also known as LAMs, proceed into the utilization of web technologies that are capable to expand the visibility and findability of their content. Within the current flourishing era of the semantic web, LAMs have voluminous amounts of web-based collections that are presented and digitally preserved through their websites. However, prior efforts indicate that LAMs suffer from fragmentation regarding the determination of well-informed strategies for improving the visibility and findability of their content on the Web (Vállez and Ventura, 2020; Krstić and Masliković, 2019; Voorbij, 2010). Several reasons related to this drawback. As such, administrators’ lack of data analytics competency in extracting and utilizing technical and behavioral datasets for improving visibility and awareness from analytics platforms; the difficulties in understanding web metrics that integrated into performance measurement systems; and hence the reduced capabilities in defining key performance indicators for greater usability, visibility, and awareness.

In this enriched and updated technical report, the authors proceed into an examination of 504 unique websites of Libraries, Archives and Museums from all over the world. It is noted that the current report has been expanded by up to 14,81% of the prior one Version 0.9.5 of 439 domains examinations. The report aims to visualize the performance of the websites in terms of technical aspects such as their adequacy to metadata description of their content and collections, their loading speed, and security. This constitutes an important stepping-stone for optimization, as the higher the alignment with the technical compliencies, the greater the users’ behavior and usability within the examined websites, and thus their findability and visibility level in search engines (Drivas et al. 2020; Mavridis and Symeonidis 2015; Agarwal et al. 2012).

One step further, within this version, we include behavioral analytics about users engagement with the content of the LAMs websites. More specifically, web analytics metrics are included such as Visit Duration, Pages per Visit, and Bounce Rates for 121 domains. We also include web analytics regarding the channels that these websites acquire their users, such as Direct traffic, Search Engines, Referral, Social Media, Email, and Display Advertising. SimilarWeb API was used to gather web data about the involved metrics.

In the first pages of this report, general information is presented regarding the names of the examined organizations. This also includes their type, their geographical location, information about the adopted Content Management Systems (CMSs), and web server software types of integration per website. Furthermore, several other data are visualized related to the size of the examined Information Organizations in terms of the number of unique webpages within a website, the number of images, internal and external links and so on.

Moreover, as a team, we proceed into the development of several factors that are capable to quantify the performance of websites. Reliability analysis takes place for measuring the internal consistency and discriminant validity of the proposed factors and their included variables. For testing the reliability, cohesion, and consistency of the included metrics, Cronbach’s Alpha (a), McDonald’s ω and Guttman λ-2 and λ-6 are used.
- For Cronbach’s, a range of .550 up to .750 indicates an acceptable level of reliability and .800 or higher a very good level (Ursachi, Horodnic, and Zait, 2015). - McDonald’s ω indicator has the advantage to measure the strength of the association between the proposed variables. More specifically, the closer to .999 the higher the strength association between the variables and vice versa (Şimşek and Noyan, 2013). - Gutman’s λ-2 and λ-6 work verifiably to Cronbach’s a as they estimate the trustworthiness of variance of the gathered web analytics metrics. Low values less than .450 indicate high bias among the harvested web metrics, while values higher than .600 and above increase the trustworthiness of the sample (Callender and Osburn, 1979). -Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test of Sphericity indicators are used for measuring the cohesion of the involved metrics. KMO and Bartlett’s test indicates that the closer the value is to .999 amongst the involved items, the higher the cohesion and consistency of them for potential categorization (Dziuban and S...

Comprehensive dataset analyzing eBay's daily visitor traffic patterns, geographic distribution, device usage, and competitive positioning based on third-party analytics from Similarweb and Semrush.

Árukereső was the most popular price comparison portal in Hungary in 2021, based on the traffic share measured by SimilarWeb. Árgép was the second most visited price comparison site over the same time period.

Определение: Измерение и классификация потока посетителей или пользователей веб-сайтов на основе различных категорий или тематик. Это включает в себя анализ и категоризацию веб-трафика с точки зрения областей или типов контента, который посетители ищут или потребляют. [Переведено с en: английского языка] Тематическая область: Информационно-коммуникационные технологии [Переведено с en: английского языка] Область применения: Веб-трафик [Переведено с en: английского языка] Примечание: Веб-трафик охватывает активность как на настольных компьютерах, так и на мобильных устройствах. Категория электронной коммерции включает трафик на сайты, относящиеся к категории электронной коммерции и торговых площадок. [Переведено с es: испанского языка] Источник данных: Цифровая обсерватория Десарролло (ODD) на основе Similarweb [Переведено с es: испанского языка] Последнее обновление: Jan 31 2024 6:02PM Организация-источник: Экономическая комиссия по Латинской Америке и Карибскому бассейну [Переведено с en: английского языка] Definition: Measurement and classification of the flow of visitors or users on websites based on different categories or topics. This involves analyzing and categorizing web traffic in terms of the areas or types of content that visitors are seeking or consuming. Thematic Area: Information and Communication Technologies Application Area: Web traffic Note: Web traffic encompasses both desktop and mobile device activity. The e-Commerce category includes traffic to sites categorized as e-Commerce and Marketplaces. Data Source: Observatorio de Desarrollo Digital (ODD) based on Similarweb Last Update: Jan 31 2024 6:02PM Source Organization: Economic Comission for Latin America and the Caribbean