Historical pricing data for SimilarWeb from 2025 to 2025. 1 data points tracking plan prices, features, and changes over time.

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 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.

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

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.

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.

Website type for the 86 websites in study.

Industry vertical of organization for 86 websites in study.

The global Advertisement Intelligence Software market is poised for substantial growth, projected to reach approximately $5,800 million by 2025, with an estimated Compound Annual Growth Rate (CAGR) of 12.5% during the forecast period of 2025-2033. This robust expansion is primarily driven by the increasing need for advertisers and brands to gain deeper insights into competitor strategies, audience behavior, and campaign performance across various digital channels. The escalating complexity of the digital advertising landscape, coupled with the proliferation of ad fraud, necessitates sophisticated intelligence solutions to optimize ad spend, enhance ROI, and maintain a competitive edge. Large enterprises, leveraging extensive marketing budgets and complex campaigns, represent a significant segment, while Small and Medium-sized Enterprises (SMEs) are increasingly adopting these tools to democratize access to competitive intelligence and level the playing field. The shift towards cloud-based solutions further fuels market adoption due to their scalability, accessibility, and cost-effectiveness, although on-premises solutions continue to cater to organizations with stringent data security and compliance requirements. The market's dynamism is further shaped by emerging trends such as the integration of AI and machine learning for predictive analytics, the demand for cross-platform advertising intelligence, and the growing focus on privacy-compliant data analysis. These advancements enable businesses to anticipate market shifts, personalize ad messaging with greater precision, and identify new growth opportunities. However, the market faces certain restraints, including the high cost of advanced features, data privacy regulations like GDPR and CCPA, and the potential for data inaccuracies if not properly managed. Geographically, North America and Europe are expected to dominate the market due to their mature digital advertising ecosystems and high adoption rates of advanced analytics tools. Asia Pacific is anticipated to witness the fastest growth, driven by its rapidly expanding digital economy and increasing investment in ad tech. Key players like Sensor Tower, IronSource, SimilarWeb, and App Annie are at the forefront, offering a comprehensive suite of solutions that empower businesses to navigate the intricate world of digital advertising with confidence and strategic advantage. This in-depth report provides a strategic overview of the global Advertisement Intelligence Software market, forecasting its trajectory from 2019 to 2033, with a base year of 2025. Our analysis delves into the intricate dynamics shaping this sector, offering actionable insights for stakeholders.