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Global network traffic analytics Industry Overview

Technavio’s analysts have identified the increasing use of network traffic analytics solutions to be one of major factors driving market growth. With the rapidly changing IT infrastructure, security hackers can steal valuable information through various modes. With the increasing dependence on web applications and websites for day-to-day activities and financial transactions, the instances of theft have increased globally. Also, the emergence of social networking websites has aided the malicious attackers to extract valuable information from vulnerable users. The increasing consumer dependence on web applications and websites for day-to-day activities and financial transactions are further increasing the risks of theft. This encourages the organizations to adopt network traffic analytics solutions.

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See the complete table of contents and list of exhibits, as well as selected illustrations and example pages from this report.

Companies covered

The network traffic analytics market is fairly concentrated due to the presence of few established companies offering innovative and differentiated software and services. By offering a complete analysis of the competitiveness of the players in the network monitoring tools market offering varied software and services, this network traffic analytics industry analysis report will aid clients identify new growth opportunities and design new growth strategies.

The report offers a complete analysis of a number of companies including:

Allot
Cisco Systems
IBM
Juniper Networks
Microsoft
Symantec

Network traffic analytics market growth based on geographic regions

Americas
APAC
EMEA

With a complete study of the growth opportunities for the companies across regions such as the Americas, APAC, and EMEA, our industry research analysts have estimated that countries in the Americas will contribute significantly to the growth of the network monitoring tools market throughout the predicted period.

Network traffic analytics market growth based on end-user

Telecom
BFSI
Healthcare
Media and entertainment

According to our market research experts, the telecom end-user industry will be the major end-user of the network monitoring tools market throughout the forecast period. Factors such as increasing use of network traffic analytics solutions and increasing use of mobile devices at workplaces will contribute to the growth of the market shares of the telecom industry in the network traffic analytics market.

Key highlights of the global network traffic analytics market for the forecast years 2018-2022:

CAGR of the market during the forecast period 2018-2022
Detailed information on factors that will accelerate the growth of the network traffic analytics market during the next five years
Precise estimation of the global network traffic analytics market size and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
Growth of the network traffic analytics industry across various geographies such as the Americas, APAC, and EMEA
A thorough analysis of the market’s competitive landscape and detailed information on several vendors
Comprehensive information about factors that will challenge the growth of network traffic analytics companies

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This market research report analyzes the market outlook and provides a list of key trends, drivers, and challenges that are anticipated to impact the global network traffic analytics market and its stakeholders over the forecast years.

The global network traffic analytics market analysts at Technavio have also considered how the performance of other related markets in the vertical will impact the size of this market till 2022. Some of the markets most likely to influence the growth of the network traffic analytics market over the coming years are the Global Network as a Service Market and the Global Data Analytics Outsourcing Market.

Technavio’s collection of market research reports offer insights into the growth of markets across various industries. Additionally, we also provide customized reports based on the specific requirement of our clients.

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Web Analytics Market Size 2025-2029

The web analytics market size is forecast to increase by USD 3.63 billion, at a CAGR of 15.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the rising preference for online shopping and the increasing adoption of cloud-based solutions. The shift towards e-commerce is fueling the demand for advanced web analytics tools that enable businesses to gain insights into customer behavior and optimize their digital strategies. Furthermore, cloud deployment models offer flexibility, scalability, and cost savings, making them an attractive option for businesses of all sizes. However, the market also faces challenges associated with compliance to data privacy and regulations. With the increasing amount of data being generated and collected, ensuring data security and privacy is becoming a major concern for businesses.
Regulatory compliance, such as GDPR and CCPA, adds complexity to the implementation and management of web analytics solutions. Companies must navigate these challenges effectively to maintain customer trust and avoid potential legal issues. To capitalize on market opportunities and address these challenges, businesses should invest in robust web analytics solutions that prioritize data security and privacy while providing actionable insights to inform strategic decision-making and enhance customer experiences.

What will be the Size of the Web Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities unfolding across various sectors. Entities such as reporting dashboards, schema markup, conversion optimization, session duration, organic traffic, attribution modeling, conversion rate optimization, call to action, content calendar, SEO audits, website performance optimization, link building, page load speed, user behavior tracking, and more, play integral roles in this ever-changing landscape. Data visualization tools like Google Analytics and Adobe Analytics provide valuable insights into user engagement metrics, helping businesses optimize their content strategy, website design, and technical SEO. Goal tracking and keyword research enable marketers to measure the return on investment of their efforts and refine their content marketing and social media marketing strategies.

Mobile optimization, form optimization, and landing page optimization are crucial aspects of website performance optimization, ensuring a seamless user experience across devices and improving customer acquisition cost. Search console and page speed insights offer valuable insights into website traffic analysis and help businesses address technical issues that may impact user behavior. Continuous optimization efforts, such as multivariate testing, data segmentation, and data filtering, allow businesses to fine-tune their customer journey mapping and cohort analysis. Search engine optimization, both on-page and off-page, remains a critical component of digital marketing, with backlink analysis and page authority playing key roles in improving domain authority and organic traffic.

The ongoing integration of user behavior tracking, click-through rate, and bounce rate into marketing strategies enables businesses to gain a deeper understanding of their audience and optimize their customer experience accordingly. As market dynamics continue to evolve, the integration of these tools and techniques into comprehensive digital marketing strategies will remain essential for businesses looking to stay competitive in the digital landscape.

How is this Web Analytics Industry segmented?

The web analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  Cloud-based
  On-premises


Application

  Social media management
  Targeting and behavioral analysis
  Display advertising optimization
  Multichannel campaign analysis
  Online marketing


Component

  Solutions
  Services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

.

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.

In today's digital landscape, web analytics plays a pivotal role in driving business growth and optimizing online performance. Cloud-based deployment of web analytics is a game-changer, enabling on-demand access to computing resources for data analysis. This model streamlines business intelligence processes by collecting, integra

Dataset

This dataset was created by Merve Afranur ARTAR

Contents

In the second quarter of 2025, mobile devices (excluding tablets) accounted for 62.54 percent of global website traffic. Since consistently maintaining a share of around 50 percent beginning in 2017, mobile usage surpassed this threshold in 2020 and has demonstrated steady growth in its dominance of global web access. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

Machine Learning as a Service Market valued at $29.41 Billion in 2023, and is projected to $USD 283.91 Billion by 2032, at a CAGR of 28.65% from 2023 to 2032.

Representative applications that can directly collect 5G da-tasets from mobile terminals without using specialized equipment include G-NetTrack Pro and PCAPdroid. The for-mer allows for the monitoring and logging of the header and payload information of the medium access control (MAC) frame passing through the 5G air interface. The latter is an open-source network capture and monitoring tool that works without root privileges, analyzing connections made by ap-plications installed on the user's mobile device. The latter can also dump mobile traffic to PCAP (also known as libpcap) and send it to the well-known Wireshark for further analysis. We created 5G datasets by measuring 5G traffic directly from a major mobile operator in South Korea. The model name of the mobile terminal used for traffic measurement is the Samsung Galaxy A90 5G, and it was equipped with a Qualcomm Snapdragon X50 5G modem. The packet sniffer software used for traffic measurement, PCAPdroid, was in-stalled in the terminal through Google play. Traffic was measured sequentially per application on two stationary ter-minals (only one terminal was used for non-interactive ser-vices) with no background traffic. The collected dataset is representative resource-intensive video traffic that has the greatest impact on 5G network planning and provisioning, and background traffic was not mixed to measure the unique characteristics of each type of traffic. The video streaming dataset includes data directly meas-ured while watching Netflix and Amazon Prime, which are representative over-the-top (OTT) services, on mobile devic-es. The live streaming dataset was measured while watching YouTube Live and South Korea's representative live broad-casts (Naver NOW and Afreeca TV). Video conferencing data were measured by holding an actual meeting on the widely used Zoom, MS Teams, and Google Meet platform. Two types of metaverse traffic were acquired: Zepeto and Roblox. Zepeto traffic was collected while staying in the 'camping world' for 15 hours. Roblox traffic was collected over 25 hours of playing the 'Collect All Pets' game using an auto clicker. We collected two types of mobile network gaming traffic. The first was cloud gaming, an online game setup that runs video games on remote servers and streams them direct-ly to the user's device. The second was a traditional mobile game connected to the Internet. The dataset was collected from May to October 2022, is a massive 328 hours in total, and is provided in the csv file format. The dataset we collected is a timestamp-mapped time series dataset with packet header information, and traffic analysis by application is possible because it includes source and destination addresses. To make it more usable as a traffic source model, Section III describes how to use it as a training dataset for the traffic simulator platform's source generator.

A 5G traffic dataset measured by PCAPdroid has been re-leased and can be used as a training dataset for various ML models. However, since the size of this dataset is very large, it is inconvenient to handle, and additional data preprocessing is required to use it for its intended purpose.

This data set can be used to learn GANs, time-series forcasting deep learning models.

Our implementation is given on GitHub. https://github.com/0913ktg/5G-Traffic-Generator

QUIC Traffic Analysis for Security Market Outlook

According to our latest research, the global QUIC Traffic Analysis for Security market size reached USD 1.24 billion in 2024, reflecting robust growth driven by increasing cyber threats and the rapid adoption of QUIC protocol in enterprise networks. The market is poised for significant expansion, projected to reach USD 4.31 billion by 2033, growing at a strong CAGR of 14.8% during the forecast period. This growth is primarily fueled by the urgent need for advanced security solutions capable of deciphering and monitoring encrypted QUIC traffic, as organizations move toward more secure and efficient internet protocols.

The rapid proliferation of QUIC (Quick UDP Internet Connections) protocol across global internet infrastructure is one of the primary growth factors for the QUIC Traffic Analysis for Security market. QUIC, originally developed by Google and now standardized by the IETF, offers significant improvements in performance, latency, and security over traditional protocols like TCP. However, these advantages also bring new security challenges, as QUIC’s encryption and multiplexing features make traditional traffic inspection and threat detection methods less effective. As a result, enterprises and security vendors are increasingly investing in advanced QUIC traffic analysis tools to maintain visibility into network activity, detect sophisticated threats, and ensure regulatory compliance. The growing reliance on cloud services, remote work environments, and high-speed internet applications is further accelerating the demand for specialized QUIC security solutions.

Another major driver for the market is the evolving threat landscape and the sophistication of cyberattacks targeting encrypted traffic. Attackers are leveraging QUIC’s encrypted channels to bypass conventional security appliances, making it difficult for organizations to identify malicious activity or data exfiltration. This has heightened the focus on next-generation security solutions that can analyze QUIC traffic without compromising user privacy or network performance. Vendors are responding by developing innovative software and hardware appliances, as well as managed security services, tailored specifically for QUIC analysis. The integration of artificial intelligence and machine learning into these solutions is also enabling faster and more accurate detection of anomalies, further boosting market growth.

Regulatory requirements and industry standards are also playing a crucial role in shaping the QUIC Traffic Analysis for Security market. As governments and industry bodies enforce stricter data protection and cybersecurity regulations, organizations are compelled to adopt more comprehensive security frameworks. This includes the ability to inspect and analyze encrypted traffic, such as QUIC, to prevent data breaches and ensure compliance with standards like GDPR, HIPAA, and PCI DSS. The increasing frequency of high-profile cyber incidents involving encrypted protocols has also raised awareness among enterprises regarding the importance of robust QUIC traffic analysis. As a result, security spending is on the rise across various sectors, including BFSI, healthcare, government, and IT, contributing to the sustained growth of this market.

From a regional perspective, North America is expected to maintain its dominance in the QUIC Traffic Analysis for Security market, driven by the presence of leading cybersecurity vendors, high adoption rates of advanced internet protocols, and stringent regulatory frameworks. However, Asia Pacific is emerging as the fastest-growing region, supported by rapid digital transformation, expanding internet infrastructure, and increasing investments in cybersecurity across countries like China, India, Japan, and South Korea. Europe is also witnessing substantial growth, fueled by GDPR compliance requirements and heightened focus on data privacy and security. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, as organizations in these regions recognize the critical need to secure their networks against evolving threats associated with QUIC traffic.

Please refer to the original data article for further data description: Jan Luxemburk et al. CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines, Data in Brief, 2023, 108888, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2023.108888. We recommend using the CESNET DataZoo python library, which facilitates the work with large network traffic datasets. More information about the DataZoo project can be found in the GitHub repository https://github.com/CESNET/cesnet-datazoo. The QUIC (Quick UDP Internet Connection) protocol has the potential to replace TLS over TCP, which is the standard choice for reliable and secure Internet communication. Due to its design that makes the inspection of QUIC handshakes challenging and its usage in HTTP/3, there is an increasing demand for research in QUIC traffic analysis. This dataset contains one month of QUIC traffic collected in an ISP backbone network, which connects 500 large institutions and serves around half a million people. The data are delivered as enriched flows that can be useful for various network monitoring tasks. The provided server names and packet-level information allow research in the encrypted traffic classification area. Moreover, included QUIC versions and user agents (smartphone, web browser, and operating system identifiers) provide information for large-scale QUIC deployment studies. Data capture The data was captured in the flow monitoring infrastructure of the CESNET2 network. The capturing was done for four weeks between 31.10.2022 and 27.11.2022. The following list provides per-week flow count, capture period, and uncompressed size:

W-2022-44

Uncompressed Size: 19 GB Capture Period: 31.10.2022 - 6.11.2022 Number of flows: 32.6M W-2022-45

Uncompressed Size: 25 GB Capture Period: 7.11.2022 - 13.11.2022 Number of flows: 42.6M W-2022-46

Uncompressed Size: 20 GB Capture Period: 14.11.2022 - 20.11.2022 Number of flows: 33.7M W-2022-47

Uncompressed Size: 25 GB Capture Period: 21.11.2022 - 27.11.2022 Number of flows: 44.1M CESNET-QUIC22

Uncompressed Size: 89 GB Capture Period: 31.10.2022 - 27.11.2022 Number of flows: 153M

Data description The dataset consists of network flows describing encrypted QUIC communications. Flows were created using ipfixprobe flow exporter and are extended with packet metadata sequences, packet histograms, and with fields extracted from the QUIC Initial Packet, which is the first packet of the QUIC connection handshake. The extracted handshake fields are the Server Name Indication (SNI) domain, the used version of the QUIC protocol, and the user agent string that is available in a subset of QUIC communications. Packet Sequences Flows in the dataset are extended with sequences of packet sizes, directions, and inter-packet times. For the packet sizes, we consider payload size after transport headers (UDP headers for the QUIC case). Packet directions are encoded as ±1, +1 meaning a packet sent from client to server, and -1 a packet from server to client. Inter-packet times depend on the location of communicating hosts, their distance, and on the network conditions on the path. However, it is still possible to extract relevant information that correlates with user interactions and, for example, with the time required for an API/server/database to process the received data and generate the response to be sent in the next packet. Packet metadata sequences have a length of 30, which is the default setting of the used flow exporter. We also derive three fields from each packet sequence: its length, time duration, and the number of roundtrips. The roundtrips are counted as the number of changes in the communication direction (from packet directions data); in other words, each client request and server response pair counts as one roundtrip. Flow statistics Flows also include standard flow statistics, which represent aggregated information about the entire bidirectional flow. The fields are: the number of transmitted bytes and packets in both directions, the duration of flow, and packet histograms. Packet histograms include binned counts of packet sizes and inter-packet times of the entire flow in both directions (more information in the PHISTS plugin documentation There are eight bins with a logarithmic scale; the intervals are 0-15, 16-31, 32-63, 64-127, 128-255, 256-511, 512-1024, >1024 [ms or B]. The units are milliseconds for inter-packet times and bytes for packet sizes. Moreover, each flow has its end reason - either it was idle, reached the active timeout, or ended due to other reasons. This corresponds with the official IANA IPFIX-specified values. The FLOW_ENDREASON_OTHER field represents the forced end and lack of resources reasons. The end of flow detected reason is not considered because it is not relevant for UDP connections. Dataset structure The dataset flows are delivered in compressed CSV files. CSV files contain one flow per row; data columns are summarized in the provided list below. For each flow data file, there is a JSON file with the number of saved and seen (before sampling) flows per service and total counts of all received (observed on the CESNET2 network), service (belonging to one of the dataset's services), and saved (provided in the dataset) flows. There is also the stats-week.json file aggregating flow counts of a whole week and the stats-dataset.json file aggregating flow counts for the entire dataset. Flow counts before sampling can be used to compute sampling ratios of individual services and to resample the dataset back to the original service distribution. Moreover, various dataset statistics, such as feature distributions and value counts of QUIC versions and user agents, are provided in the dataset-statistics folder. The mapping between services and service providers is provided in the servicemap.csv file, which also includes SNI domains used for ground truth labeling. The following list describes flow data fields in CSV files:

ID: Unique identifier SRC_IP: Source IP address DST_IP: Destination IP address DST_ASN: Destination Autonomous System number SRC_PORT: Source port DST_PORT: Destination port PROTOCOL: Transport protocol QUIC_VERSION QUIC: protocol version QUIC_SNI: Server Name Indication domain QUIC_USER_AGENT: User agent string, if available in the QUIC Initial Packet TIME_FIRST: Timestamp of the first packet in format YYYY-MM-DDTHH-MM-SS.ffffff TIME_LAST: Timestamp of the last packet in format YYYY-MM-DDTHH-MM-SS.ffffff DURATION: Duration of the flow in seconds BYTES: Number of transmitted bytes from client to server BYTES_REV: Number of transmitted bytes from server to client PACKETS: Number of packets transmitted from client to server PACKETS_REV: Number of packets transmitted from server to client PPI: Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]] PPI_LEN: Number of packets in the PPI sequence PPI_DURATION: Duration of the PPI sequence in seconds PPI_ROUNDTRIPS: Number of roundtrips in the PPI sequence PHIST_SRC_SIZES: Histogram of packet sizes from client to server PHIST_DST_SIZES: Histogram of packet sizes from server to client PHIST_SRC_IPT: Histogram of inter-packet times from client to server PHIST_DST_IPT: Histogram of inter-packet times from server to client APP: Web service label CATEGORY: Service category FLOW_ENDREASON_IDLE: Flow was terminated because it was idle FLOW_ENDREASON_ACTIVE: Flow was terminated because it reached the active timeout FLOW_ENDREASON_OTHER: Flow was terminated for other reasons

Link to other CESNET datasets

https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/ https://github.com/CESNET/cesnet-datazoo Please cite the original data article:

@article{CESNETQUIC22, author = {Jan Luxemburk and Karel Hynek and Tomáš Čejka and Andrej Lukačovič and Pavel Šiška}, title = {CESNET-QUIC22: a large one-month QUIC network traffic dataset from backbone lines}, journal = {Data in Brief}, pages = {108888}, year = {2023}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2023.108888}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923000069} }

HTPA Dataset

Dataset Name: HTPA(HTTPS Tunneling Predictive Analysis Dataset)

Table of Contents

1. Overview
2. Dataset Structure
3. Data Collection Methodology
4. Privacy Considerations
5. Feature Descriptions
6. Usage Instructions
7. Contact

Overview

The HTPA dataset is designed for research and development in detecting HTTPS tunnel traffic versus normal HTTPS traffic. It is especially suitable for knowledge-graph-based algorithms, such as the HINT method, due to its inclusion of multi-dimensional traffic features and large-scale network interactions.

Key Features

• HTTPS tunnel traffic, VPN.

Dataset Structure

The HTPA dataset is provided as a compressed file with the following structure:

HTPA/
├── tunnel_traffic.pickle 
├── normal_traffic.pickle 
├── load_data.py 
└── splited_data/                    
  ├── test_data_split_by_date.pickle 
  ├── test_data_split_by_service.pickle 
  ├── train_data_split_by_date.pickle 
  └── train_data_split_by_service.pickle 

Data Collection Methodology

HTPA was generated to capture diverse traffic interactions in a server-client setup. Tunnel traffic data was gathered from popular VPN services—Hotspot Shield Free, Browsec VPN, ZenMate VPN, Hoxx VPN, and ShadowsocksR—all of which utilize HTTPS tunneling for data transfer. In collecting HTTPS tunnel traffic, we developed a crawler script that automated the process of visiting websites via these VPNs. To simulated realistic user behavior patterns, the crawler script was designed to browse at preset intervals with random pauses, closely mimicking human interaction habits. Specifically, clients (computers and mobile devices) were connected to VPN servers with configured client software. The crawler then launched a Chrome browser to visit randomly chosen sites from the Alexa Top 10,000 websites. All traffic was routed through a configured router that mirrored it to a storage server, archiving it in pcap format, and this process was repeated multiple times to ensure dataset diversity.

For non-VPN (normal HTTPS) traffic, data was collected passively from a corporate network environment with over one hundred users. Five volunteers logged their regular online activities without VPNs or proxies over a month, allowing us to record their service IPs and ports as non-VPN traffic.

Privacy Considerations

Due to the dataset's scale and privacy concerns, we provide extracted features rather than raw network packets. All IP addresses and domain names have been hashed to preserve anonymity.

Feature Descriptions

The feature data files (tunnel_traffic.pickle and normal_traffic.pickle) include:

Usage Instructions

In past experiments, using a simple random sampling to split train and test data led to very high accuracy (around 0.99) for some baseline models like Random Forest. However, in real-world scenarios, models face greater challenges, including unknown data and concept drift. To better mimic open-world scenarios, we suggest using the following two data split strategies:

By Service: Here, we used 'ServerIP' and 'ServerPort' as unique identifiers for each service, ensuring the same service wasn’t present in both train and test data. This approach prevent...

During a 2024 survey among marketers worldwide, around 86 percent reported using Facebook for marketing purposes. Instagram and LinkedIn followed, respectively mentioned by 79 and 65 percent of the respondents.

              The global social media marketing segment

              According to the same study, 59 percent of responding marketers intended to increase their organic use of YouTube for marketing purposes throughout that year. LinkedIn and Instagram followed with similar shares, rounding up the top three social media platforms attracting a planned growth in organic use among global marketers in 2024. Their main driver is increasing brand exposure and traffic, which led the ranking of benefits of social media marketing worldwide.

              Social media for B2B marketing

              Social media platform adoption rates among business-to-consumer (B2C) and business-to-business (B2B) marketers vary according to each subsegment's focus. While B2C professionals prioritize Facebook and Instagram – both run by Meta, Inc. – due to their popularity among online audiences, B2B marketers concentrate their endeavors on Microsoft-owned LinkedIn due to its goal to connect people and companies in a corporate context.

As of February 2025, it was found that around 14.1 percent of TikTok's global audience were women between the ages of 18 and 24 years, while male users of the same age formed approximately 16.6 percent of the platform's audience. The online audience of the popular social video platform was further composed of 14.6 percent of female users aged between 25 and 34 years, and 20.7 percent of male users in the same age group.

During a 2024 survey among marketers worldwide, approximately 83 percent selected increased exposure as a benefit of social media marketing. Increased traffic followed, mentioned by 73 percent of the respondents, while 65 percent cited generated leads.

              The multibillion-dollar social media ad industry

              Between 2019 – the last year before the pandemic – and 2024, global social media advertising spending skyrocketed by 140 percent, surpassing an estimated 230 billion U.S. dollars in the latter year. That figure was forecast to increase by nearly 50 percent by the end of the decade, exceeding 345 billion dollars in 2029. As of 2024, the social media networks with the most monthly active users were Facebook, with over three billion, and YouTube, with more than 2.5 billion.

              Pros and cons of GenAI for social media marketing

              According to another 2024 survey, generative artificial intelligence's (GenAI) leading benefits for social media marketing according to professionals worldwide included increased efficiency and easier idea generation. The third place was a tie between increased content production and enhanced creativity. All those advantages were cited by between 33 and 38 percent of the interviewees. As for GenAI's top challenges for global social media marketing,
              maintaining authenticity and the value of human creativity ranked first, mentioned by 43 and 40 percent of the respondents, respectively. Another 35 percent deemed ensuring the content resonates as an obstacle.

As of January 2024, #love was the most used hashtag on Instagram, being included in over two billion posts on the social media platform. #Instagood and #instagram were used over one billion times as of early 2024.

During a January 2024 global survey among marketers, nearly 60 percent reported plans to increase their organic use of YouTube for marketing purposes in the following 12 months. LinkedIn and Instagram followed, respectively mentioned by 57 and 56 percent of the respondents intending to use them more. According to the same survey, Facebook was the most important social media platform for marketers worldwide.

As of October 2025, Google represented ***** percent of the global online search engine referrals on desktop devices. Despite being much ahead of its competitors, this represents a modest increase from the previous months. Meanwhile, its longtime competitor Bing accounted for ***** percent, as tools like Yahoo and Yandex held shares of over **** percent and **** percent respectively. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of **** trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly ****** billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than ** percent of internet users in Russia used Yandex, whereas Google users represented little over ** percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over ** percent of users in Mexico said they used Yahoo.

PeeringDB Analytics Services Market Outlook

According to our latest research, the global PeeringDB Analytics Services market size reached USD 1.37 billion in 2024, exhibiting a robust year-on-year expansion. The market is projected to grow at a CAGR of 13.1% during the forecast period, reaching USD 3.64 billion by 2033. The primary growth factor driving this market is the increasing demand for optimized network performance and data-driven interconnection strategies among Internet Service Providers, data centers, and cloud service providers globally.

The growth trajectory of the PeeringDB Analytics Services market is underpinned by the exponential surge in global internet traffic, the proliferation of data centers, and the growing complexity of network interconnections. As organizations strive to deliver seamless digital experiences, the need for advanced analytics to monitor, manage, and optimize peering arrangements has become paramount. PeeringDB Analytics Services enable stakeholders to gain actionable insights into traffic flows, identify bottlenecks, and enhance network efficiency, which is critical in today’s hyper-connected landscape. The adoption of cloud-based infrastructure and the emergence of new digital business models further amplify the reliance on these analytics services, solidifying their importance in modern networking ecosystems.

Another significant growth factor is the increasing regulatory scrutiny and compliance requirements in the telecommunications and internet infrastructure sectors. Regulatory bodies are mandating greater transparency and reporting standards for network operators and service providers, which necessitates robust analytics platforms capable of delivering comprehensive compliance and reporting functionalities. PeeringDB Analytics Services offer automated compliance tracking and reporting features, reducing the administrative burden on organizations and ensuring adherence to evolving industry standards. This regulatory push is expected to further accelerate the adoption of analytics services across various end-user segments, particularly among large enterprises and multinational corporations.

The rapid digital transformation initiatives across emerging markets, coupled with the expansion of cloud computing, are also fueling the demand for PeeringDB Analytics Services. Enterprises are increasingly leveraging hybrid and multi-cloud environments, necessitating sophisticated analytics to manage complex interconnections and optimize network performance. The growing emphasis on customer experience, coupled with the need for real-time performance monitoring, is compelling organizations to invest in advanced analytics solutions. This trend is particularly pronounced in sectors such as finance, healthcare, and e-commerce, where uninterrupted connectivity and network reliability are mission-critical.

From a regional perspective, North America currently dominates the PeeringDB Analytics Services market, driven by the presence of leading technology firms, advanced digital infrastructure, and a mature ecosystem of internet exchange points. However, Asia Pacific is poised for the fastest growth over the forecast period, propelled by rapid digitalization, increased investments in data center infrastructure, and the expansion of internet connectivity across developing economies. Europe also holds a significant market share, supported by stringent regulatory frameworks and a strong focus on network optimization among enterprises. Latin America and the Middle East & Africa are emerging as promising markets, with growing awareness of the benefits of analytics-driven network management.

Component Analysis

The Component segment of the PeeringDB Analytics Services market is bifurcated into Software and Services, each playing a pivotal role in the overall ecosystem. The software component encompasses a range of analytics platforms, dashboards, and visualization tools designed to collect, process, and interpret network data from PeeringDB an

During the fourth quarter 2024, approximately 20.6 million TikTok accounts were removed from the platform due to suspicion of being operated by users under the age of 13. During the last measured period, around 185 million fake accounts were removed from fake accounts removed from TikTok.

As of June 2025, Google accounted for 94.8 percent of the search engine web traffic in Spain, being the most popular tool of this kind in the country across all devices. Bing was the second-main search engine, with 3.18 percent of the web traffic, while Yahoo! followed with 1.11 percent.

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Data Center Transformation Market Size 2024-2028

The data center transformation market size is forecast to increase by USD 17.8 billion at a CAGR of 23.5% between 2023 and 2028.

The market is experiencing significant growth due to several key drivers. Hyperscalers' demand for data centers is expanding, leading to increased construction and optimization of these facilities. Data center infrastructure management (DCIM) and automation are becoming increasingly popular to optimize IT resources and improve overall efficiency. Security concerns, particularly around server security, are also driving market growth. Additionally, the trend toward cloud computing and data center colocation is continuing, with businesses seeking to leverage the benefits of cloud services while maintaining control over their data. Proactive maintenance and optimization of the data center ecosystem are essential to ensure business continuity and mitigate potential risks. As the market evolves, organizations must stay informed about the latest trends and challenges to make informed decisions about their data center strategies.

What will be the Size of the Market During the Forecast Period?

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Data centers play a crucial role in the digital transformation of businesses, enabling the deployment and operation of IT resources, including cloud-based architectures and on-premises systems. The landscape is evolving, with an increasing focus on proactive maintenance, data security, and IT resources optimization. Data Center Infrastructure Management (DCIM) is a key aspect of data center transformation. DCIM tools help organizations manage their ecosystem, ensuring efficient use of resources and reducing downtime. Proactive maintenance, achieved through DCIM, is essential for minimizing disruptions and ensuring program availability. Data security is another critical concern for businesses undergoing data center transformation. With the rise of cloud traffic and the Internet of Things (IoT), securing data centers against cyber threats is more important than ever. Software-optimized data centers and IT infrastructures can help mitigate risks, ensuring data privacy and compliance.


Similarly, data center relocation is another trend. As businesses grow, they may need to move their data centers to accommodate their expanding IT needs. Colocation providers offer solutions for businesses looking to relocate their data centers, providing access to cloud service providers and enterprise resource planning (ERP) systems. Cloud-based architectures and SaaS-based applications are driving data center traffic, necessitating the optimization of data centers to handle increased demand. Big data analytics is another factor contributing to transformation, requiring large amounts of storage and processing power. IT infrastructure optimization is a key objective for businesses undergoing transformation. Local data centers offer the advantage of reduced latency and improved performance for applications that require real-time processing. Data center services, including hardware configurations and cooling systems, are essential for optimizing IT infrastructures. Moreover, cloud traffic, IoT devices, and big data analytics all contribute to this complexity.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Servers
  Power management
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Application Insights

The servers segment is estimated to witness significant growth during the forecast period.

Data centers are essential infrastructure for businesses to manage and process their IT resources effectively. Servers, a key component of data centers, facilitate the running of applications, data processing, and data storage. Servers comprise processors, memory, network, and various ports. They can function as storage servers by integrating direct-attached storage (DAS), network-attached storage (NAS), or storage-attached network (SAN) devices. In today's digital landscape, data centers undergo continuous transformation to optimize IT resources and enhance data security. Cloud computing has significantly impacted the data center ecosystem, leading to the adoption of cloud services, colocation, and data center relocation. Proactive maintenance is a crucial aspect of data center optimization, ensuring uninterrupted operations and minimizing downtime.

Furthermore, web servers, a type of data center server, are responsible for hosting domain names and IP addresses. They utilize hypertext transfer

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.