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The dataset contains information about web requests to a single website. It's a time series dataset, which means it tracks data over time, making it great for machine learning analysis.
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Discover the booming website visitor tracking software market! Our analysis reveals a $5 billion market in 2025, projected to reach $15 billion by 2033, driven by digital marketing, data-driven decisions, and AI-powered analytics. Learn about key players, market trends, and regional insights.
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TwitterA dataset explaining organic traffic, its importance for SEO, and methods to track it in Google Analytics 4.
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Twitterhttps://semrush.ebundletools.com/company/legal/terms-of-service/https://semrush.ebundletools.com/company/legal/terms-of-service/
track.tw is ranked #2871 in TW with 163.31K Traffic. Categories: . Learn more about website traffic, market share, and more!
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Competitive Analysis of Industry Rivals The market for competitive analysis is expected to grow significantly over the forecast period, driven by increasing need for businesses to understand their competitive landscape. Key players in the market include BuiltWith, WooRank, SEMrush, Google, SpyFu, Owletter, SimilarWeb, Moz, SunTec Data, and TrendSource. These companies offer a range of services to help businesses track their competitors' online performance, including website traffic, social media engagement, and search engine rankings. Some of the key trends driving the growth of the market include the increasing adoption of digital marketing by businesses, the growing importance of social media, and the increasing availability of data and analytics tools. The market is segmented by type, application, and region. In terms of type, the market is divided into product analysis, traffic analytics, sales analytics, and others. In terms of application, the market is divided into SMEs and large enterprises. In terms of region, the market is divided into North America, South America, Europe, Middle East & Africa, and Asia Pacific. The North American region is expected to dominate the market during the forecast period, due to the presence of a large number of established players in the market. The Asia Pacific region is expected to grow at the highest CAGR during the forecast period, due to the increasing adoption of digital marketing by businesses in the region. This report provides a comprehensive analysis of the industry rivals, encompassing their concentration, product insights, regional trends, and key industry developments.
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| BASE YEAR | 2024 |
| HISTORICAL DATA | 2019 - 2023 |
| REGIONS COVERED | North America, Europe, APAC, South America, MEA |
| REPORT COVERAGE | Revenue Forecast, Competitive Landscape, Growth Factors, and Trends |
| MARKET SIZE 2024 | 2.48(USD Billion) |
| MARKET SIZE 2025 | 2.64(USD Billion) |
| MARKET SIZE 2035 | 5.0(USD Billion) |
| SEGMENTS COVERED | Tool Type, Deployment Type, End User, Feature Set, Regional |
| COUNTRIES COVERED | US, Canada, Germany, UK, France, Russia, Italy, Spain, Rest of Europe, China, India, Japan, South Korea, Malaysia, Thailand, Indonesia, Rest of APAC, Brazil, Mexico, Argentina, Rest of South America, GCC, South Africa, Rest of MEA |
| KEY MARKET DYNAMICS | growing digital marketing demand, increasing data analysis needs, rising competition among businesses, advancements in analytics technology, need for real-time insights |
| MARKET FORECAST UNITS | USD Billion |
| KEY COMPANIES PROFILED | Rank Ranger, SimilarWeb, Moz, Ahrefs, Alexa, Compete, SEMrush, Quantcast, Comscore, Statista, Traffic Well, Serpstat, Moz Pro, SiteWorthTraffic, KeenStats, SpyFu |
| MARKET FORECAST PERIOD | 2025 - 2035 |
| KEY MARKET OPPORTUNITIES | AI-driven analytics integration, Enhanced mobile optimization features, Real-time traffic monitoring capabilities, Multi-channel traffic source tracking, Customizable reporting solutions |
| COMPOUND ANNUAL GROWTH RATE (CAGR) | 6.6% (2025 - 2035) |
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A complete list of live websites using the Track Visitors technology, compiled through global website indexing conducted by WebTechSurvey.
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Explore the booming Website Visitor Tracking Software market, driven by AI and cloud adoption. Discover key insights, market size projections, and CAGR growth up to 2033. Optimize your digital strategy with actionable analytics.
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TwitterIn 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.
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track.global is ranked #13467 in RU with 360.25K Traffic. Categories: . Learn more about website traffic, market share, and more!
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TwitterAttribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
License information was derived automatically
This dataset originates from DataCamp. Many users have reposted copies of the CSV on Kaggle, but most of those uploads omit the original instructions, business context, and problem framing. In this upload, I’ve included that missing context in the About Dataset so the reader of my notebook or any other notebook can fully understand how the data was intended to be used and the intended problem framing.
Note: I have also uploaded a visualization of the workflow I personally took to tackle this problem, but it is not part of the dataset itself.
Additionally, I created a PowerPoint presentation based on my work in the notebook, which you can download from here:
PPTX Presentation
From: Head of Data Science
Received: Today
Subject: New project from the product team
Hey!
I have a new project for you from the product team. Should be an interesting challenge. You can see the background and request in the email below.
I would like you to perform the analysis and write a short report for me. I want to be able to review your code as well as read your thought process for each step. I also want you to prepare and deliver the presentation for the product team - you are ready for the challenge!
They want us to predict which recipes will be popular 80% of the time and minimize the chance of showing unpopular recipes. I don't think that is realistic in the time we have, but do your best and present whatever you find.
You can find more details about what I expect you to do here. And information on the data here.
I will be on vacation for the next couple of weeks, but I know you can do this without my support. If you need to make any decisions, include them in your work and I will review them when I am back.
Good Luck!
From: Product Manager - Recipe Discovery
To: Head of Data Science
Received: Yesterday
Subject: Can you help us predict popular recipes?
Hi,
We haven't met before but I am responsible for choosing which recipes to display on the homepage each day. I have heard about what the data science team is capable of and I was wondering if you can help me choose which recipes we should display on the home page?
At the moment, I choose my favorite recipe from a selection and display that on the home page. We have noticed that traffic to the rest of the website goes up by as much as 40% if I pick a popular recipe. But I don't know how to decide if a recipe will be popular. More traffic means more subscriptions so this is really important to the company.
Can your team: - Predict which recipes will lead to high traffic? - Correctly predict high traffic recipes 80% of the time?
We need to make a decision on this soon, so I need you to present your results to me by the end of the month. Whatever your results, what do you recommend we do next?
Look forward to seeing your presentation.
Tasty Bytes was founded in 2020 in the midst of the Covid Pandemic. The world wanted inspiration so we decided to provide it. We started life as a search engine for recipes, helping people to find ways to use up the limited supplies they had at home.
Now, over two years on, we are a fully fledged business. For a monthly subscription we will put together a full meal plan to ensure you and your family are getting a healthy, balanced diet whatever your budget. Subscribe to our premium plan and we will also deliver the ingredients to your door.
This is an example of how a recipe may appear on the website, we haven't included all of the steps but you should get an idea of what visitors to the site see.
Tomato Soup
Servings: 4
Time to make: 2 hours
Category: Lunch/Snack
Cost per serving: $
Nutritional Information (per serving) - Calories 123 - Carbohydrate 13g - Sugar 1g - Protein 4g
Ingredients: - Tomatoes - Onion - Carrot - Vegetable Stock
Method: 1. Cut the tomatoes into quarters….
The product manager has tried to make this easier for us and provided data for each recipe, as well as whether there was high traffic when the recipe was featured on the home page.
As you will see, they haven't given us all of the information they have about each recipe.
You can find the data here.
I will let you decide how to process it, just make sure you include all your decisions in your report.
Don't forget to double check the data really does match what they say - it might not.
| Column Name | Details |
|---|---|
| recipe | Numeric, unique identifier of recipe |
| calories | Numeric, number of calories |
| carbohydrate | Numeric, amount of carbohydrates in grams |
| sugar | Numeric, amount of sugar in grams |
| protein | Numeric, amount of prote... |
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Swash clickstream data offers a comprehensive and GDPR-compliant dataset sourced from users worldwide, encompassing both desktop and mobile browsing behaviour. Here's an in-depth look at what sets us apart and how our data can benefit your organisation.
User-Centric Approach: Unlike traditional data collection methods, we take a user-centric approach by rewarding users for the data they willingly provide. This unique methodology ensures transparent data collection practices, encourages user participation, and establishes trust between data providers and consumers.
Wide Coverage and Varied Categories: Our clickstream data covers diverse categories, including search, shopping, and URL visits. Whether you are interested in understanding user preferences in e-commerce, analysing search behaviour across different industries, or tracking website visits, our data provides a rich and multi-dimensional view of user activities.
GDPR Compliance and Privacy: We prioritise data privacy and strictly adhere to GDPR guidelines. Our data collection methods are fully compliant, ensuring the protection of user identities and personal information. You can confidently leverage our clickstream data without compromising privacy or facing regulatory challenges.
Market Intelligence and Consumer Behaviuor: Gain deep insights into market intelligence and consumer behaviour using our clickstream data. Understand trends, preferences, and user behaviour patterns by analysing the comprehensive user-level, time-stamped raw or processed data feed. Uncover valuable information about user journeys, search funnels, and paths to purchase to enhance your marketing strategies and drive business growth.
High-Frequency Updates and Consistency: We provide high-frequency updates and consistent user participation, offering both historical data and ongoing daily delivery. This ensures you have access to up-to-date insights and a continuous data feed for comprehensive analysis. Our reliable and consistent data empowers you to make accurate and timely decisions.
Custom Reporting and Analysis: We understand that every organisation has unique requirements. That's why we offer customisable reporting options, allowing you to tailor the analysis and reporting of clickstream data to your specific needs. Whether you need detailed metrics, visualisations, or in-depth analytics, we provide the flexibility to meet your reporting requirements.
Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.
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TwitterThis 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.
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TwitterCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
A time-series quantitative dataset designed to track and document the total traffic and visits directed to the official website on a monthly basis throughout the entire year of 2025
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Twitterhttps://semrush.ebundletools.com/company/legal/terms-of-service/https://semrush.ebundletools.com/company/legal/terms-of-service/
tracking.my is ranked #253 in MY with 1.78M Traffic. Categories: Transportation and Logistics. Learn more about website traffic, market share, and more!
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TwitterThis dataset provides the Austin Google Analytic. Google Analytics is a freemium web analytics service offered by Google that tracks and reports website traffic.
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TwitterApache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
This dataset contains network traffic logs captured by Burp-Suite, aimed at classifying web requests as either good or bad based on their characteristics. The dataset is designed for the task of predicting whether incoming requests are legitimate (good) or malicious (bad), aiding in the detection and prevention of web-based attacks.
badwords = ['sleep', 'uid', 'select', 'waitfor', 'delay', 'system', 'union', 'order by', 'group by', 'admin', 'drop', 'script']
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TwitterTraffic analytics, rankings, and competitive metrics for al-monitor.com as of September 2025
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al-monitor.com is ranked #86925 in US with 302.31K Traffic. Categories: Newspapers. Learn more about website traffic, market share, and more!
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TwitterKeyword feed is created by filtering raw data through a specified keyword configuration and allows for tracking web traffic with respect to various topics, e.g.: - public companies - brands - products By analyzing the feed, it is possible to evaluate popularity and sentiment surrounding the chosen phrase over time.
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Twitterhttps://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
The dataset contains information about web requests to a single website. It's a time series dataset, which means it tracks data over time, making it great for machine learning analysis.