Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.

This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.

Data dictionary: Page_Title: Title of webpage used for pages of the website www.cityofrochester.gov Pageviews: Total number of pages viewed over the course of the calendar year listed in the year column. Repeated views of a single page are counted. Unique_Pageviews: Unique Pageviews - The number of sessions during which a specified page was viewed at least once. A unique pageview is counted for each URL and page title combination. Avg_Time: Average amount of time users spent looking at a specified page or screen. Entrances: The number of times visitors entered the website through a specified page.Bounce_Rate: " A bounce is a single-page session on your site. In Google Analytics, a bounce is calculated specifically as a session that triggers only a single request to the Google Analytics server, such as when a user opens a single page on your site and then exits without triggering any other requests to the Google Analytics server during that session. Bounce rate is single-page sessions on a page divided by all sessions that started with that page, or the percentage of all sessions on your site in which users viewed only a single page and triggered only a single request to the Google Analytics server. These single-page sessions have a session duration of 0 seconds since there are no subsequent hits after the first one that would let Google Analytics calculate the length of the session. "Exit_Rate: The number of exits from a page divided by the number of pageviews for the page. This is inclusive of sessions that started on different pages, as well as “bounce” sessions that start and end on the same page. For all pageviews to the page, Exit Rate is the percentage that were the last in the session. Year: Calendar year over which the data was collected. Data reflects the counts for each metric from January 1st through December 31st.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

Google dominated the web analytics industry in 2024, with ***** of its web analytics technologies maintaining the top three positions in the global market. Google Global Site Tag was first with a market share of over ** percent, followed by Google Analytics and Google Universal Analytics who had market shares of approximately ** and ** percent, respectively. When all ***** technologies were combined, Google maintained more than ** percent of the total market share.

Information about pages on the City's website including their age and their Google Analytics data (everything from "PageViews" and to the right). If the Google Analytics fields are empty, the page hasn't been visited recently at all.

Author: Víctor Yeste. Universitat Politècnica de Valencia.The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in online media and the possible prediction of the selected success variables.In this case, due to the need to integrate data from two separate areas, such as web publishing and the analysis of their shares and related topics on Twitter, has opted for programming as you access both the Google Analytics v4 reporting API and Twitter Standard API, always respecting the limits of these.The website analyzed is hellofriki.com. It is an online media whose primary intention is to solve the need for information on some topics that provide daily a vast number of news in the form of news, as well as the possibility of analysis, reports, interviews, and many other information formats. All these contents are under the scope of the sections of cinema, series, video games, literature, and comics.This dataset has contributed to the elaboration of the PhD Thesis:Yeste Moreno, VM. (2021). Diseño de una metodología cibermétrica de cálculo del éxito para la optimización de contenidos web [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176009Data have been obtained from each last-minute news article published online according to the indicators described in the doctoral thesis. All related data are stored in a database, divided into the following tables:tesis_followers: User ID list of media account followers.tesis_hometimeline: data from tweets posted by the media account sharing breaking news from the web.status_id: Tweet IDcreated_at: date of publicationtext: content of the tweetpath: URL extracted after processing the shortened URL in textpost_shared: Article ID in WordPress that is being sharedretweet_count: number of retweetsfavorite_count: number of favoritestesis_hometimeline_other: data from tweets posted by the media account that do not share breaking news from the web. Other typologies, automatic Facebook shares, custom tweets without link to an article, etc. With the same fields as tesis_hometimeline.tesis_posts: data of articles published by the web and processed for some analysis.stats_id: Analysis IDpost_id: Article ID in WordPresspost_date: article publication date in WordPresspost_title: title of the articlepath: URL of the article in the middle webtags: Tags ID or WordPress tags related to the articleuniquepageviews: unique page viewsentrancerate: input ratioavgtimeonpage: average visit timeexitrate: output ratiopageviewspersession: page views per sessionadsense_adunitsviewed: number of ads viewed by usersadsense_viewableimpressionpercent: ad display ratioadsense_ctr: ad click ratioadsense_ecpm: estimated ad revenue per 1000 page viewstesis_stats: data from a particular analysis, performed at each published breaking news item. Fields with statistical values can be computed from the data in the other tables, but total and average calculations are saved for faster and easier further processing.id: ID of the analysisphase: phase of the thesis in which analysis has been carried out (right now all are 1)time: "0" if at the time of publication, "1" if 14 days laterstart_date: date and time of measurement on the day of publicationend_date: date and time when the measurement is made 14 days latermain_post_id: ID of the published article to be analysedmain_post_theme: Main section of the published article to analyzesuperheroes_theme: "1" if about superheroes, "0" if nottrailer_theme: "1" if trailer, "0" if notname: empty field, possibility to add a custom name manuallynotes: empty field, possibility to add personalized notes manually, as if some tag has been removed manually for being considered too generic, despite the fact that the editor put itnum_articles: number of articles analysednum_articles_with_traffic: number of articles analysed with traffic (which will be taken into account for traffic analysis)num_articles_with_tw_data: number of articles with data from when they were shared on the media’s Twitter accountnum_terms: number of terms analyzeduniquepageviews_total: total page viewsuniquepageviews_mean: average page viewsentrancerate_mean: average input ratioavgtimeonpage_mean: average duration of visitsexitrate_mean: average output ratiopageviewspersession_mean: average page views per sessiontotal: total of ads viewedadsense_adunitsviewed_mean: average of ads viewedadsense_viewableimpressionpercent_mean: average ad display ratioadsense_ctr_mean: average ad click ratioadsense_ecpm_mean: estimated ad revenue per 1000 page viewsTotal: total incomeretweet_count_mean: average incomefavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesterms_ini_num_tweets: total tweets on the terms on the day of publicationterms_ini_retweet_count_total: total retweets on the terms on the day of publicationterms_ini_retweet_count_mean: average retweets on the terms on the day of publicationterms_ini_favorite_count_total: total of favorites on the terms on the day of publicationterms_ini_favorite_count_mean: average of favorites on the terms on the day of publicationterms_ini_followers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the terms on the day of publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms on the day of publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who spoke about the terms on the day of publicationterms_ini_user_age_mean: average age in days of users who have spoken of the terms on the day of publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms on the day of publicationterms_end_num_tweets: total tweets on terms 14 days after publicationterms_ini_retweet_count_total: total retweets on terms 14 days after publicationterms_ini_retweet_count_mean: average retweets on terms 14 days after publicationterms_ini_favorite_count_total: total bookmarks on terms 14 days after publicationterms_ini_favorite_count_mean: average of favorites on terms 14 days after publicationterms_ini_followers_talking_rate: ratio of media Twitter account followers who have recently posted a tweet talking about the terms 14 days after publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms 14 days after publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who have spoken about the terms 14 days after publicationterms_ini_user_age_mean: the average age in days of users who have spoken of the terms 14 days after publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms 14 days after publication.tesis_terms: data of the terms (tags) related to the processed articles.stats_id: Analysis IDtime: "0" if at the time of publication, "1" if 14 days laterterm_id: Term ID (tag) in WordPressname: Name of the termslug: URL of the termnum_tweets: number of tweetsretweet_count_total: total retweetsretweet_count_mean: average retweetsfavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesfollowers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the termuser_num_followers_mean: average followers of users who were talking about the termuser_num_tweets_mean: average number of tweets published by users who were talking about the termuser_age_mean: average age in days of users who were talking about the termurl_inclusion_rate: URL inclusion ratio

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

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

Contains view count data for the top 20 pages each day on the Somerville MA city website dating back to 2020. Data is used in the City's dashboard which can be found at https://www.somervilledata.farm/.

Snapshot img

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.
Request Free Sample

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

Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

The ga-report extension for CKAN is designed to provide detailed Google Analytics reports, including totals per group, to site managers. Unlike other extensions that focus on providing real-time page view statistics for end-users, this extension focuses on building regular, periodic reports to monitor site usage and performance. It enables site administrators to download Google Analytics data into CKAN's database tables and view the data as web page reports, thereby facilitating informed decision-making and resource allocation. Key Features: Google Analytics Data Download: Enables users to download Google Analytics data for specified time periods using a CLI tool, storing this data within the extension's database tables. Web Page Reports: Allows users to view the downloaded Google Analytics data as web page reports accessible through the CKAN interface. Periodic Report Generation: Emphasizes the creation of regular, periodic reports instead of focusing solely on real-time analytics, offering a historical perspective on site traffic and engagement. Bounce Rate Tracking: Specifies a particular URL (typically the homepage) to record and analyze bounce rates for, enabling optimization of landing pages. Data Retrieval Customization: Supports retrieving data for all time, the latest available data, or data from a specific date, providing flexibility in data analysis. Technical Integration: Requires setting up Google Analytics and obtaining API credentials that are then used to access Google Analytics data. This involves enabling the "Analytics API" in the Google APIs Console and creating an OAuth 2.0 client ID and secret. The extension utilizes a credentials.json file to store authentication details, allowing the CKAN instance to securely access Google Analytics. The location of the token.dat authentication token generated is specified in the CKAN configuration file (development.ini or similar). The extension's database tables are initialized using a paster command, which ensures that the required data structures are set up within CKAN's database to store the Google Analytics data. Benefits & Impact: By providing a mechanism for regular Google Analytics reporting, the ga-report extension assists CKAN site managers in monitoring trends, identifying areas for improvement, and making data-driven decisions to optimize site performance. The ability to download and store Google Analytics data within CKAN also allows for more in-depth analysis and integration with other data sources.

Website type for the 86 websites in study.

all-clad.com is ranked #20052 in US with 0 Traffic. Categories: Retail. Learn more about website traffic, market share, and more!

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 Shirkey, 1974). Both descriptive statistics and reliability analyses were performed via JASP 0.14.1.0 software.

To this end, this report contributes to the knowledge expansion of all the interest parties and stakeholders related to the research topic of improving the visibility and findability of LAMs and their content on the Web. It constitutes a well-informed compass, that could be adopted by such organizations, in order to implement potential strategies that combine both domain knowledge and data-driven culture in terms of awareness optimization on the internet realm.

The Research Team Behind the Project

The whole project is managed and optimized on a weekly basis by a big young and smiley team of scientists (alphabetically referred in the next section). All of them are undergraduate students at the Department of Archival, Library and Information Studies of the University of West Attica.

They are responsible for the overall process of publishing the Technical Report which includes the initial organizations’ identification, and subsequently, websites testing, data gathering, curation and pre-processing, analysis, validation and visualization. Of course, the Team will continue to expand the capabilities of this report while involving new features, metrics, and further information regarding Libraries, Archives and Museums websites from all over the world.

Notice: includes a plurality of technical and behavioral factors and variables concerning the examined information organizations' websites. Potentially, more features will be included on the next versions.

Report Version 0.9.6 Correspondence: Ioannis C. Drivas (PhDc) idrivas@uniwa.gr | http://users.uniwa.gr/idrivas/ Research Lab of Information Management Department of Archival, Library Science and Information Studies University of West Attica.

Snapshot img

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.

Want a bigger picture? Try a FREE sample of this report now!

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

Get more value with Technavio’s INSIGHTS subscription platform! Gain easy access to all of Technavio’s reports, along with on-demand services. Try the demo

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.

Monthly analytics reports for the Brisbane City Council website

Information regarding the sessions for Brisbane City Council website during the month including the number of active users and views.

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

Monthly analytics reports for the Brisbane City Council website

Information regarding the sessions for Brisbane City Council website during the month including search terms used.

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

Monthly analytics reports for the Brisbane City Council website

Information regarding the sessions for Brisbane City Council website during the month including page views and unique page views.