Web traffic statistics for the top 2000 most visited pages on nyc.gov by month.

The Web Analytics Market Report is Segmented by Application ( Mobile Analytics, Content Marketing, Social Media Management, and More), Offering (Solutions, and Services), Deployment Model (Cloud-Based, and On-Premises), Organization Size (Large Enterprises, and Small and Medium Enterprises), End-User Vertical (Retail and E-Commerce, Manufacturing, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

This Website Statistics dataset has four resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file.

• Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year.

• Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year.

• Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year.

• Dataset Statistics: This dataset shows cumulative totals for Datasets on the Lincolnshire Open Data site that have also been published on the national Open Data site Data.Gov.UK - see the Source link.

  Note: Website and Webpage statistics (the first three resources above) show only UK users, and exclude API calls (automated requests for datasets). The Dataset Statistics are confined to users with javascript enabled, which excludes web crawlers and API calls.

These Website Statistics resources are updated annually in January by the Lincolnshire County Council Business Intelligence team. For any enquiries about the information contact opendata@lincolnshire.gov.uk.

The global web analytics market size was USD 7.40 billion in 2024 & is projected to grow from USD 8.79 billion in 2025 to USD 34.88 billion by 2033.
Report Scope:

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.

The size of the Web Analytics Tools market was valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2032, with an expected CAGR of XX% during the forecast period.

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/.

The global web analytics market, valued at $5529.7 million in 2025, is poised for substantial growth. While the provided CAGR is missing, considering the rapid advancements in digital technologies and the increasing reliance on data-driven decision-making across industries, a conservative estimate would place the Compound Annual Growth Rate (CAGR) between 15% and 20% for the forecast period 2025-2033. This growth is fueled by several key drivers: the rising adoption of cloud-based analytics solutions, the increasing demand for real-time data insights, and the growing need for personalized customer experiences. Furthermore, the expansion of e-commerce and the proliferation of mobile devices are significantly contributing to the market's expansion. Emerging trends such as artificial intelligence (AI) and machine learning (ML) integration within web analytics platforms are further enhancing analytical capabilities and driving market growth. While challenges like data privacy concerns and the complexity of integrating diverse data sources exist, the overall market outlook remains positive, suggesting a significant increase in market value by 2033. The competitive landscape is dynamic, with a mix of established players like Adobe, Google, and IBM alongside agile startups like Heap and Mouseflow. These companies offer a range of solutions catering to different business sizes and needs, from basic website traffic analysis to sophisticated predictive analytics. The market is witnessing a shift towards more user-friendly and visually appealing dashboards, making web analytics accessible to a broader range of users beyond dedicated data scientists. This democratization of data, coupled with ongoing technological advancements, promises to further accelerate market growth and consolidate the position of web analytics as a critical component of successful digital strategies across all sectors.

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.

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.

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

Recipe Site Traffic

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.

About Tasty Bytes

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.

Example Recipe

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….

Data Information

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.

This Website Statistics dataset has three resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file. Please Note: due to a change in Analytics platform and accompanying metrics, the current files do not contain a full years data. The files will be updated again in January 2025 with 2024-2025 data. The previous dataset containing Web Analytics has been archived and can be found in the following link; https://lincolnshire.ckan.io/dataset/website-statistics-archived Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year. Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year. Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year. Note: The resources above exclude API calls (automated requests for datasets). These Website Statistics resources are updated annually in February by the Lincolnshire County Council Open Data team.

Explore the booming Web Analytics market, projected to reach USD 3144.1 million by 2025 with a 10.4% CAGR. Discover key drivers, applications like social media management, and regional growth trends. Optimize your digital strategy with data-driven insights.

The global Enterprise Website Analytics Software market is experiencing robust growth, driven by the increasing need for businesses to understand their online presence and optimize their digital strategies. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the proliferation of mobile devices and diverse digital channels requiring sophisticated analytics, and a growing focus on data-driven decision-making across all departments. Large enterprises are leading the adoption, leveraging these tools for detailed customer journey mapping, performance optimization, and enhanced ROI on marketing investments. However, the market faces challenges such as the complexity of integrating various analytics platforms and the need for specialized expertise to effectively interpret and utilize the vast amounts of data generated. The segment showing the fastest growth is likely cloud-based solutions due to their flexibility and accessibility. We estimate the 2025 market size to be around $15 billion, based on observable growth trends in related software markets and considering the increasing adoption of analytics solutions across various industries. A Compound Annual Growth Rate (CAGR) of 12% is projected for the forecast period (2025-2033), indicating substantial market expansion over the coming years. The competitive landscape is highly dynamic, with both established tech giants (Google, IBM) and specialized analytics providers (Adobe, SEMrush, Mixpanel) vying for market share. The ongoing trend towards mergers and acquisitions further shapes the industry. Companies are continually innovating to offer more comprehensive solutions, incorporating features like artificial intelligence (AI) for predictive analytics, real-time data visualization, and seamless integration with CRM systems. Geographic growth will vary, with North America and Europe expected to maintain significant market share due to high technological adoption rates. However, Asia-Pacific is projected to witness substantial growth driven by increasing digitalization and economic expansion. The market's future trajectory hinges on continuous innovation within analytics capabilities, addressing the challenges of data privacy and security, and fostering greater user-friendliness within these sophisticated platforms.

Demographic Analysis of Shopping Behavior: Insights and Recommendations

Dataset Information: The Shopping Mall Customer Segmentation Dataset comprises 15,079 unique entries, featuring Customer ID, age, gender, annual income, and spending score. This dataset assists in understanding customer behavior for strategic marketing planning.

Cleaned Data Details: Data cleaned and standardized, 15,079 unique entries with attributes including - Customer ID, age, gender, annual income, and spending score. Can be used by marketing analysts to produce a better strategy for mall specific marketing.

Challenges Faced: 1. Data Cleaning: Overcoming inconsistencies and missing values required meticulous attention. 2. Statistical Analysis: Interpreting demographic data accurately demanded collaborative effort. 3. Visualization: Crafting informative visuals to convey insights effectively posed design challenges.

Research Topics: 1. Consumer Behavior Analysis: Exploring psychological factors driving purchasing decisions. 2. Market Segmentation Strategies: Investigating effective targeting based on demographic characteristics.

Suggestions for Project Expansion: 1. Incorporate External Data: Integrate social media analytics or geographic data to enrich customer insights. 2. Advanced Analytics Techniques: Explore advanced statistical methods and machine learning algorithms for deeper analysis. 3. Real-Time Monitoring: Develop tools for agile decision-making through continuous customer behavior tracking. This summary outlines the demographic analysis of shopping behavior, highlighting key insights, dataset characteristics, team contributions, challenges, research topics, and suggestions for project expansion. Leveraging these insights can enhance marketing strategies and drive business growth in the retail sector.

References OpenAI. (2022). ChatGPT [Computer software]. Retrieved from https://openai.com/chatgpt. Mustafa, Z. (2022). Shopping Mall Customer Segmentation Data [Data set]. Kaggle. Retrieved from https://www.kaggle.com/datasets/zubairmustafa/shopping-mall-customer-segmentation-data Donkeys. (n.d.). Kaggle Python API [Jupyter Notebook]. Kaggle. Retrieved from https://www.kaggle.com/code/donkeys/kaggle-python-api/notebook Pandas-Datareader. (n.d.). Retrieved from https://pypi.org/project/pandas-datareader/

Access Market Research Intellect's Enterprise Website Analytics Software Market Report for insights on a market worth USD 3.5 billion in 2024, expanding to USD 8.1 billion by 2033, driven by a CAGR of 12.8%.Learn about growth opportunities, disruptive technologies, and leading market participants.

Global Web Analytics market size 2021 was recorded $4083.04 Million whereas by the end of 2025 it will reach $7985.3 Million. According to the author, by 2033 Web Analytics market size will become $30542.6. Web Analytics market will be growing at a CAGR of 18.257% during 2025 to 2033.

The global web analytics tools market is projected to reach a valuation of USD 8.5 billion by 2033, growing at a compound annual growth rate (CAGR) of 14.2% from 2025 to 2033.

The global Website Analytics Tool market is poised for significant expansion, projected to reach approximately $18,200 million by 2025, driven by a robust Compound Annual Growth Rate (CAGR) of 12.5%. This impressive growth trajectory, anticipated to continue through 2033, is fueled by the escalating need for businesses of all sizes to gain actionable insights into user behavior, website performance, and digital marketing effectiveness. The proliferation of online businesses, the increasing reliance on data-driven decision-making, and the constant evolution of digital strategies are paramount drivers. Small and Medium-sized Enterprises (SMEs) represent a substantial segment, increasingly adopting these tools to level the playing field with larger competitors by optimizing their online presence and customer engagement. Simultaneously, large enterprises are leveraging advanced analytics to refine complex customer journeys, personalize user experiences, and achieve greater ROI from their digital investments. The shift towards cloud-based solutions is a defining trend, offering scalability, accessibility, and cost-effectiveness, thereby democratizing access to sophisticated analytics capabilities. Further propelling market growth is the inherent demand for sophisticated functionalities such as real-time tracking, A/B testing, heat mapping, and user session recording, enabling deeper comprehension of user interactions. Key restraints, such as data privacy concerns and the initial cost of implementation for some advanced solutions, are being mitigated by evolving data protection regulations and the growing availability of freemium and tiered pricing models. The competitive landscape is characterized by the presence of both established giants like Google Analytics and Adobe Analytics, and innovative players like Matomo, Mixpanel, and Hotjar, each offering unique value propositions. Regions like North America and Europe are leading the adoption curve due to their mature digital economies and strong emphasis on data analytics, while the Asia Pacific region is emerging as a high-growth market driven by rapid digital transformation and a burgeoning e-commerce sector. The continuous innovation in AI and machine learning is expected to further enhance the predictive and prescriptive capabilities of website analytics tools, solidifying their indispensable role in modern business strategy. This comprehensive report offers an in-depth analysis of the global Website Analytics Tool market, projecting significant growth and evolving dynamics. The study encompasses a Study Period from 2019 to 2033, with a Base Year and Estimated Year of 2025, and a Forecast Period from 2025 to 2033, building upon historical data from 2019 to 2024. Our analysis delves into market concentration, key trends, regional dominance, product innovations, and the strategic maneuvers of leading players, providing invaluable insights for stakeholders.

Note: This dataset has been archived and is no longer being updated due to a change in analytics platform. You can find the new dataset relating to Website Statistics in the following link; https://lincolnshire.ckan.io/dataset/website-statistics This Website Statistics dataset has three resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file. Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year. Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year. Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year. Note: The resources above show only UK users, and exclude API calls (automated requests for datasets). For further information, please contact the Lincolnshire County Council Open Data team.