According to research from SimilarWeb, Google Chrome, the browser app led the list of trending apps among free apps for iPads on the India Apple App Store as of *********. Spotify, the music streaming platform followed in the list during the same time period.

According to research from SimilarWeb, Amaziograph, the artwork app led the list of trending apps among the paid iPad apps on the India Apple App Store as of *********. Affinity Photo, the graphic editor app was followed in the list during the same time period.

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

According to research from SimilarWeb, Tinder, the dating app led the list of trending apps among the top grossers for iPhone on the India Apple App Store as of *********. Azar, the video chat app was followed in the list during the same time period.

According to research from SimilarWeb, Snapchat, the messaging app led the list of trending apps among the free iPhone apps on the India Apple App Store as of June 2021. Zomato, the food delivery app rounded up the list during the same time period.

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

The global Digital Ad Intelligence Software market is experiencing robust growth, projected to reach $1021.7 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 6.8% from 2025 to 2033. This expansion is driven by several key factors. The increasing complexity of digital advertising channels necessitates sophisticated tools for campaign optimization, performance measurement, and competitive analysis. Businesses, particularly large enterprises and SMEs, are increasingly adopting these solutions to improve ROI and gain a competitive edge. Furthermore, the rise of programmatic advertising and the growing volume of digital ad data have fueled the demand for advanced analytics and insights. The market is segmented by deployment type (cloud-based and on-premises) and user type (large enterprises and SMEs), with cloud-based solutions witnessing faster adoption due to their scalability and cost-effectiveness. Geographical growth is diverse, with North America currently holding a significant market share, followed by Europe and Asia Pacific. However, rapid digitalization in emerging markets is expected to significantly boost market growth in these regions over the forecast period. The competitive landscape is fragmented, with numerous players offering a range of specialized solutions, fostering innovation and competition. Continued growth in the market is expected to be fueled by several trends. The increasing sophistication of ad fraud detection techniques within the software is a key driver. Further, integration with other marketing technology (MarTech) platforms enhances efficiency and data-driven decision-making, thereby driving adoption. However, challenges such as high initial investment costs, data security concerns, and the need for specialized skills to utilize the software effectively could potentially restrain market growth. Nevertheless, the overall outlook for the Digital Ad Intelligence Software market remains positive, driven by the continuous evolution of the digital advertising landscape and the ever-increasing need for data-driven insights to optimize marketing campaigns.

According to research from SimilarWeb, Minecraft, the gaming app led the list of apps that were on the trending down list among paid iPhone apps on the India Apple App Store as of June 2021. Sticker Babai, the Telugu stickers app also lost popularity in terms of store ranks during the same time period.

BackgroundPrimary dysmenorrhea (PD), common in women below 25 years, occurs as pain in the absence of any identifiable pelvic pathology. Menstrual tracking applications (MTAs) may help women manage their PD symptoms. No systematic assessment has been performed on MTA quality with respect to physical therapy management exercise.ObjectivesThis study evaluated the quality of MTAs available in Saudi Arabia for mobile users in both the App Store and Google Play Store and assessed the quality and completeness of exercise regimens provided in these apps using the FITT principle as a guideline for managing PD symptoms.MethodsIn this cross-sectional study, apps were collected from the App Store and Google Play Store using two strategies for each store independently: Scraper and SimilarWeb. The app quality was evaluated using the Mobile Application Rating Scale (MARS), and exercise content was evaluated based on the recommended Frequency, Intensity, Time, and Type (FITT) principles.ResultsFinal evaluation included 16 apps, of which 87.5% required subscription. The mean app quality score ranged from 2.54 (worst-rated app) to 4.45 (best-rated app) with a mean score of 3.54 ± 0.58. In addition, only three apps provided all the FITT components in the exercise content.ConclusionThis study assessed the quality of exercise provided within these applications as interventions for managing PD symptoms. This evaluation contributes to the understanding of mobile health technologies for PD management in the region, and highlights areas for improvement in app development and content quality to better serve individuals with PD.

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

According to research from SimilarWeb, Procreate, the graphic editor app led the list of trending apps among the top grossing iPad apps on the India Apple App Store as of *********. Notability, for note-taking ranked eleventh during the same time period.

BackgroundPrimary dysmenorrhea (PD), common in women below 25 years, occurs as pain in the absence of any identifiable pelvic pathology. Menstrual tracking applications (MTAs) may help women manage their PD symptoms. No systematic assessment has been performed on MTA quality with respect to physical therapy management exercise.ObjectivesThis study evaluated the quality of MTAs available in Saudi Arabia for mobile users in both the App Store and Google Play Store and assessed the quality and completeness of exercise regimens provided in these apps using the FITT principle as a guideline for managing PD symptoms.MethodsIn this cross-sectional study, apps were collected from the App Store and Google Play Store using two strategies for each store independently: Scraper and SimilarWeb. The app quality was evaluated using the Mobile Application Rating Scale (MARS), and exercise content was evaluated based on the recommended Frequency, Intensity, Time, and Type (FITT) principles.ResultsFinal evaluation included 16 apps, of which 87.5% required subscription. The mean app quality score ranged from 2.54 (worst-rated app) to 4.45 (best-rated app) with a mean score of 3.54 ± 0.58. In addition, only three apps provided all the FITT components in the exercise content.ConclusionThis study assessed the quality of exercise provided within these applications as interventions for managing PD symptoms. This evaluation contributes to the understanding of mobile health technologies for PD management in the region, and highlights areas for improvement in app development and content quality to better serve individuals with PD.

According to research from SimilarWeb, Project Makeover, the gaming app led the list of apps that were on the trending down list among free iPad apps on the India Apple App Store as of *********. Messenger for WhatsApp iPad also lost popularity in terms of store ranks during the same time period.

Market Analysis for Advertising Intelligence Tools The global advertising intelligence tool market is expanding rapidly, driven by the increasing need for businesses to measure and optimize their advertising campaigns. The market size was valued at $XXX million in 2019 and is projected to reach $XXX million by 2033, at a CAGR of XX%. The growth is attributed to the rise of digital marketing, the increasing complexity of advertising campaigns, and the need for businesses to gain insights into their target audience. The market is segmented by application (large enterprises, SMEs) and type (cloud-based, on-premises). Key drivers include: growing adoption of digital advertising, increasing use of data analytics, and rising investment in advertising by businesses. The competitive landscape is fragmented, with a mix of established vendors and emerging players. Some of the major companies operating in the market include Semrush, Adbeat, PowerAdSpy, Sensor Tower, AdMobiSpy, Anstrex, SocialPeta, Oracle, iSpionage, Pathmatics, Soomla, Similarweb, BIScience, WhatRunsWhere.com, Mobile Action, Numerator, adjinn, Admetricks, App Annie, Apptica, Apptopia, BrandTotal, Kantar, Macaw.pro, Nielsen, and others. The market is expected to witness strategic partnerships and acquisitions as well as ongoing technological advancements, such as the integration of AI and machine learning. Companies are focusing on providing comprehensive solutions that meet the evolving needs of businesses, enabling them to gain a competitive edge in the advertising landscape.

According to research from SimilarWeb, Telegram Messenger, the messaging app led the list of apps that were on the trending down list among the free iPhone apps on the India Apple App Store as of *********. Messenger, the messaging app from Facebook also lost popularity in terms of store ranks during the same time period.

According to research from SimilarWeb, Peeppa Pig: Party Time, the entertainment app led the list of apps that were on the trending down list among paid iPad apps on the India Apple App Store as of *********. Out There, the gaming app also lost popularity in terms of store ranks during the same time period.

According to research from SimilarWeb, Teen Patti Gold, the family card game app led the list of apps that were on the trending down list among the top grossing ones on the India Google Play Store as of *********. Zynga Poker, the card game app also lost popularity in terms of usage ranks during the same time period.

According to research from SimilarWeb, CamScanner, the scanning app led the list of apps that were on the trending down list among paid apps on the India Google Play Store as of *********. Stickman Legends, the premium offline gaming app also lost popularity in terms of usage ranks during the same time period.

According to research from SimilarWeb, Messenger, the text and video chat app from Facebook led the list of apps that were on the trending down list among the free ones on the India Google Play Store as of *********. Hotstar, the video streaming app also lost popularity in terms of usage ranks during the same time period.