According to the results of a survey conducted worldwide in 2023, nearly **** of responding digital marketers believed artificial intelligence (AI) would have a positive impact on website search traffic in the next five years. Some ** percent stated AI would have a neutral effect, while ** percent agreed that the technology would negatively impact search traffic.

27% website traffic decline for non-optimized businesses

Click Web Traffic Combined with Transaction Data: A New Dimension of Shopper Insights

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. Click enhances the unparalleled accuracy of CE Transact by allowing investors to delve deeper and browse further into global online web traffic for CE Transact companies and more. Leverage the unique fusion of web traffic and transaction datasets to understand the addressable market and understand spending behavior on consumer and B2B websites. See the impact of changes in marketing spend, search engine algorithms, and social media awareness on visits to a merchant’s website, and discover the extent to which product mix and pricing drive or hinder visits and dwell time. Plus, Click uncovers a more global view of traffic trends in geographies not covered by Transact. Doubleclick into better forecasting, with Click.

Consumer Edge’s Click is available in machine-readable file delivery and enables: • Comprehensive Global Coverage: Insights across 620+ brands and 59 countries, including key markets in the US, Europe, Asia, and Latin America. • Integrated Data Ecosystem: Click seamlessly maps web traffic data to CE entities and stock tickers, enabling a unified view across various business intelligence tools. • Near Real-Time Insights: Daily data delivery with a 5-day lag ensures timely, actionable insights for agile decision-making. • Enhanced Forecasting Capabilities: Combining web traffic indicators with transaction data helps identify patterns and predict revenue performance.

Use Case: Analyze Year Over Year Growth Rate by Region

Problem A public investor wants to understand how a company’s year-over-year growth differs by region.

Solution The firm leveraged Consumer Edge Click data to: • Gain visibility into key metrics like views, bounce rate, visits, and addressable spend • Analyze year-over-year growth rates for a time period • Breakout data by geographic region to see growth trends

Metrics Include: • Spend • Items • Volume • Transactions • Price Per Volume

Inquire about a Click subscription to perform more complex, near real-time analyses on public tickers and private brands as well as for industries beyond CPG like: • Monitor web traffic as a leading indicator of stock performance and consumer demand • Analyze customer interest and sentiment at the brand and sub-brand levels

Consumer Edge offers a variety of datasets covering the US, Europe (UK, Austria, France, Germany, Italy, Spain), and across the globe, with subscription options serving a wide range of business needs.

Consumer Edge is the Leader in Data-Driven Insights Focused on the Global Consumer

In 2024, most of the global website traffic was still generated by humans, but bot traffic is constantly growing. Fraudulent traffic through bad bot actors accounted for 37 percent of global web traffic in the most recently measured period, representing an increase of 12 percent from the previous year. Sophistication of Bad Bots on the rise The complexity of malicious bot activity has dramatically increased in recent years. Advanced bad bots have doubled in prevalence over the past 2 years, indicating a surge in the sophistication of cyber threats. Simultaneously, the share of simple bad bots drastically increased over the last years, suggesting a shift in the landscape of automated threats. Meanwhile, areas like food and groceries, sports, gambling, and entertainment faced the highest amount of advanced bad bots, with more than 70 percent of their bot traffic affected by evasive applications. Good and bad bots across industries The impact of bot traffic varies across different sectors. Bad bots accounted for over 50 percent of the telecom and ISPs, community and society, and computing and IT segments web traffic. However, not all bot traffic is considered bad. Some of these applications help index websites for search engines or monitor website performance, assisting users throughout their online search. Therefore, areas like entertainment, food and groceries, and even areas targeted by bad bots themselves experienced notable levels of good bot traffic, demonstrating the diverse applications of benign automated systems across different sectors.

From October 2024 to February 2025, ChatGPT outperformed competing AI-powered search engines in traffic referral, achieving a total growth of 155.52 percent. Perplexity placed second, despite experiencing more significant fluctuations, with a total growth of 54.78 percent by the conclusion of the analyzed period. With a 43.64 percent overall growth, Google's Gemini ranked third among other engines and maintained the most consistent traffic referral rate. Artificial intelligence-driven trends, notably AI-powered search, are changing online traffic patterns. This suggests a more significant change in the way users find information online and is expected to have a knock-on effect on the digital advertising sector.

Unlock the Potential of Your Web Traffic with Advanced Data Resolution

In the digital age, understanding and leveraging web traffic data is crucial for businesses aiming to thrive online. Our pioneering solution transforms anonymous website visits into valuable B2B and B2C contact data, offering unprecedented insights into your digital audience. By integrating our unique tag into your website, you unlock the capability to convert 25-50% of your anonymous traffic into actionable contact rows, directly deposited into an S3 bucket for your convenience. This process, known as "Web Traffic Data Resolution," is at the forefront of digital marketing and sales strategies, providing a competitive edge in understanding and engaging with your online visitors.

Comprehensive Web Traffic Data Resolution Our product stands out by offering a robust solution for "Web Traffic Data Resolution," a process that demystifies the identities behind your website traffic. By deploying a simple tag on your site, our technology goes to work, analyzing visitor behavior and leveraging proprietary data matching techniques to reveal the individuals and businesses behind the clicks. This innovative approach not only enhances your data collection but does so with respect for privacy and compliance standards, ensuring that your business gains insights ethically and responsibly.

Deep Dive into Web Traffic Data At the core of our solution is the sophisticated analysis of "Web Traffic Data." Our system meticulously collects and processes every interaction on your site, from page views to time spent on each section. This data, once anonymous and perhaps seen as abstract numbers, is transformed into a detailed ledger of potential leads and customer insights. By understanding who visits your site, their interests, and their contact information, your business is equipped to tailor marketing efforts, personalize customer experiences, and streamline sales processes like never before.

Benefits of Our Web Traffic Data Resolution Service Enhanced Lead Generation: By converting anonymous visitors into identifiable contact data, our service significantly expands your pool of potential leads. This direct enhancement of your lead generation efforts can dramatically increase conversion rates and ROI on marketing campaigns.

Targeted Marketing Campaigns: Armed with detailed B2B and B2C contact data, your marketing team can create highly targeted and personalized campaigns. This precision in marketing not only improves engagement rates but also ensures that your messaging resonates with the intended audience.

Improved Customer Insights: Gaining a deeper understanding of your web traffic enables your business to refine customer personas and tailor offerings to meet market demands. These insights are invaluable for product development, customer service improvement, and strategic planning.

Competitive Advantage: In a digital landscape where understanding your audience can make or break your business, our Web Traffic Data Resolution service provides a significant competitive edge. By accessing detailed contact data that others in your industry may overlook, you position your business as a leader in customer engagement and data-driven strategies.

Seamless Integration and Accessibility: Our solution is designed for ease of use, requiring only the placement of a tag on your website to start gathering data. The contact rows generated are easily accessible in an S3 bucket, ensuring that you can integrate this data with your existing CRM systems and marketing tools without hassle.

How It Works: A Closer Look at the Process Our Web Traffic Data Resolution process is streamlined and user-friendly, designed to integrate seamlessly with your existing website infrastructure:

Tag Deployment: Implement our unique tag on your website with simple instructions. This tag is lightweight and does not impact your site's loading speed or user experience.

Data Collection and Analysis: As visitors navigate your site, our system collects web traffic data in real-time, analyzing behavior patterns, engagement metrics, and more.

Resolution and Transformation: Using advanced data matching algorithms, we resolve the collected web traffic data into identifiable B2B and B2C contact information.

Data Delivery: The resolved contact data is then securely transferred to an S3 bucket, where it is organized and ready for your access. This process occurs daily, ensuring you have the most up-to-date information at your fingertips.

Integration and Action: With the resolved data now in your possession, your business can take immediate action. From refining marketing strategies to enhancing customer experiences, the possibilities are endless.

Security and Privacy: Our Commitment Understanding the sensitivity of web traffic data and contact information, our solution is built with security and privacy at its core. We adhere to strict data protection regulat...

As of April 2020, it was estimated that the web traffic could increase by up to 25 percent in Argentina and 20 percent in Brazil, compared to the average prior to the COVID-19 outbreak. In Colombia and Ecuador, fixed-line internet traffic was expected to increase by 40 and 30 percent, respectively.

Artificial Intelligence in Computer Networks Market Outlook

The global artificial intelligence in computer networks market size is projected to experience substantial growth, with an estimated market value of USD 12.5 billion in 2023, expected to reach approximately USD 48.2 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 16%. The primary growth factor driving this market is the increasing demand for enhanced network performance and security amid expanding digital transformation initiatives across various industries. As more enterprises recognize the potential of AI to revolutionize network management, optimization, and security, the integration of AI into computer networks continues to rise significantly.

A critical growth factor in this market is the burgeoning need for improved network optimization techniques to handle the massive influx of data generated by modern businesses. As organizations transition to digital infrastructures, the complexity of managing networks intensifies, necessitating intelligent solutions capable of automating routine tasks and optimizing network performance. AI technologies offer capabilities such as real-time traffic analysis, dynamic bandwidth allocation, and anomaly detection, which are crucial for maintaining efficient and reliable network operations. As networks become more complex, the ability of AI to predict and resolve network issues before they impact performance is becoming an invaluable asset to businesses worldwide.

Furthermore, the rising incidence of cyber threats and the increasing sophistication of cyberattacks underscore the critical role AI plays in enhancing network security. Artificial intelligence, with its capacity to analyze vast datasets for patterns and anomalies, is instrumental in identifying potential security breaches and unauthorized access in real-time. This proactive approach to security is increasingly preferred over traditional methods, which are often reactive and insufficient in the face of rapidly evolving threats. Consequently, industries such as BFSI, healthcare, and telecommunications are heavily investing in AI-enhanced security solutions to safeguard their networks against an ever-growing array of cyber threats.

The advent of predictive maintenance capabilities powered by AI is also a significant growth catalyst in this market. Predictive maintenance minimizes downtime by forecasting potential equipment failures and suggesting timely maintenance activities. By leveraging AI's ability to process historical data and identify trends, organizations can maintain optimal network conditions, reduce operational costs, and extend the lifespan of their infrastructure. This technology is particularly beneficial in industries reliant on complex networking equipment, such as telecommunications and IT, where unexpected equipment failure can result in substantial financial losses and service disruptions.

Optimizing Networks has become a cornerstone in the evolution of AI in computer networks. As the volume and complexity of data traffic continue to rise, businesses are increasingly turning to AI-driven solutions to enhance network efficiency and reliability. These solutions leverage advanced algorithms to predict network congestion, optimize data flow, and allocate resources dynamically, ensuring seamless connectivity and minimal downtime. This optimization not only improves user experience but also reduces operational costs by making better use of existing infrastructure. As organizations strive to maintain competitive advantage in a rapidly digitalizing world, the role of AI in optimizing networks is set to expand, driving further innovation and investment in this critical area.

Regionally, North America currently holds the largest share of the artificial intelligence in computer networks market, driven by the early adoption of advanced technologies and the presence of leading AI technology providers. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, with a CAGR of 18%, as countries like China and India increase their investments in AI technology to bolster network infrastructure and digital transformation initiatives. Europe also shows promising growth potential, supported by strong government initiatives and increasing awareness of AI benefits in network management and security.

Component Analysis

The AI in computer networks market is segmented by components into software, hardware, and

Overview

This record provides a dataset created as part of the study presented in the following publication and is made publicly available for research purposes. The associated article provides a comprehensive description of the dataset, its structure, and the methodology used in its creation. If you use this dataset, please cite the following article published in the journal IEEE Communications Magazine:

A. Karamchandani, J. Nunez, L. de-la-Cal, Y. Moreno, A. Mozo, and A. Pastor, “On the Applicability of Network Digital Twins in Generating Synthetic Data for Heavy Hitter Discrimination,” IEEE Communications Magazine, pp. 2–8, 2025, DOI: 10.1109/MCOM.003.2400648.

More specifically, the record contains several synthetic datasets generated to differentiate between benign and malicious heavy hitter flows within a realistic virtualized network environment. Heavy Hitter flows, which include high-volume data transfers, can significantly impact network performance, leading to congestion and degraded quality of service. Distinguishing legitimate heavy hitter activity from malicious Distributed Denial-of-Service traffic is critical for network management and security, yet existing datasets lack the granularity needed for training machine learning models to effectively make this distinction.

To address this, a Network Digital Twin (NDT) approach was utilized to emulate realistic network conditions and traffic patterns, enabling automated generation of labeled data for both benign and malicious HH flows alongside regular traffic.

Feature Set:

The feature set includes the following flow statistics commonly used in the literature on network traffic classification:

• The protocol used for the connection, identifying whether it is TCP, UDP, ICMP, or OSPF.
• The time (relative to the connection start) of the most recent packet sent from source to destination at the time of each snapshot.
• The time (relative to the connection start) of the most recent packet sent from destination to source at the time of each snapshot.
• The cumulative count of data packets sent from source to destination at the time of each snapshot.
• The cumulative count of data packets sent from destination to source at the time of each snapshot.
• The cumulative bytes sent from source to destination at the time of each snapshot.
• The cumulative bytes sent from destination to source at the time of each snapshot.
• The time difference between the first packet sent from source to destination and the first packet sent from destination to source.

Dataset Variations:

To accommodate diverse research needs and scenarios, the dataset is provided in the following variations:

1. All at Once:

   1. Contains a synthetic dataset where all traffic types, including benign, normal, and malicious DDoS heavy hitter (HH) flows, are combined into a single dataset.
   2. This version represents a holistic view of the traffic environment, simulating real-world scenarios where all traffic occurs simultaneously.

2. Balanced Traffic Generation:

   1. Represents a balanced traffic dataset with an equal proportion of benign, normal, and malicious DDoS traffic.
   2. Designed for scenarios where a balanced dataset is needed for fair training and evaluation of machine learning models.

3. DDoS at Intervals:

   1. Contains traffic data where malicious DDoS HH traffic occurs at specific time intervals, mimicking real-world attack patterns.
   2. Useful for studying the impact and detection of intermittent malicious activities.

4. Only Benign HH Traffic:

   1. Includes only benign HH traffic flows.
   2. Suitable for training and evaluating models to identify and differentiate benign heavy hitter traffic patterns.

5. Only DDoS Traffic:

   1. Contains only malicious DDoS HH traffic.
   2. Helps in isolating and analyzing attack characteristics for targeted threat detection.

6. Only Normal Traffic:

   1. Comprises only regular, non-HH traffic flows.
   2. Useful for understanding baseline network behavior in the absence of heavy hitters.

7. Unbalanced Traffic Generation:

   1. Features an unbalanced dataset with varying proportions of benign, normal, and malicious traffic.
   2. Simulates real-world scenarios where certain types of traffic dominate, providing insights into model performance in unbalanced conditions.

For each variation, the output of the different packet aggregators is provided separated in its respective folder.

Each variation was generated using the NDT approach to demonstrate its flexibility and ensure the reproducibility of our study's experiments, while also contributing to future research on network traffic patterns and the detection and classification of heavy hitter traffic flows. The dataset is designed to support research in network security, machine learning model development, and applications of digital twin technology.

With the rise in vehicle ownership, traffic congestion has emerged as a major barrier to urban progress, making the study and optimization of urban road capacity exceedingly crucial. The research on the medium and long-term free-flowing capacity and queue emission rate of roads takes an in-depth exploration of this issue from a cutting-edge perspective, aiming to find solutions adaptable to the progression of the times. The purpose of this study is to understand and predict the road capacity and queue emission rate more accurately, thus improving the urban traffic condition. Existing literature primarily focuses on short-term forecasts of road capacity, leaving a notable void in the research of medium and long-term road capacity and queue emission rate. This gap often results in a lack of sufficient foresight when urban traffic planning faces practical issues. To fill this void, this study undertook an in-depth examination of the road capacity and queue emission rate over the medium and long term (10 years) based on big data analysis and artificial intelligence theories. This paper employs a Radial Basis Function (RBF) neural network, combined with twelve other parameters that could potentially impact road capacity, such as traffic volume, road width, number of lanes, traffic signal control methods, etc., to analyze the relationship between each parameter and free-flow traffic and queue emission rate. These analyses are grounded in extensive road data, encompassing not only the city’s main roads but also secondary roads and community roads. The study results show a continuous downward trend in the free-flowing capacity of roads and a slight upward trend in the queue emission rate over the past decade. Further analysis reveals the extent of impact each factor has on the free-flow traffic and queue emission rate, providing a scientific basis for future urban traffic planning.

Telecommunications providers are looking to Artificial intelligence (AI) to enhance all aspects of their operations. According to a 2024 global survey, telecoms firms have seen the greatest impact in network operations, where AI can help anticipate demand and optimize traffic.

AI-Enhanced Optical Line Monitoring Market Outlook

According to our latest research, the global AI-Enhanced Optical Line Monitoring market size reached USD 1.37 billion in 2024, with a robust year-over-year growth momentum driven by the rapid digital transformation across industries. The market is expected to grow at a CAGR of 13.6% from 2025 to 2033. By 2033, the market is forecasted to attain a value of USD 4.20 billion. This significant growth is attributed to the increasing demand for high-speed data transmission, network reliability, and proactive fault detection, all of which are being revolutionized by AI-powered solutions in optical line monitoring.

The growth of the AI-Enhanced Optical Line Monitoring market is primarily propelled by the exponential rise in data traffic and the widespread adoption of fiber optic networks. As global internet usage and connected devices continue to surge, telecom operators and enterprises are under mounting pressure to ensure seamless and uninterrupted network performance. AI-driven optical line monitoring systems provide real-time insights, predictive maintenance, and automated fault localization, reducing downtime and operational costs. The integration of AI enables advanced analytics and machine learning capabilities, allowing operators to proactively address network issues before they impact service quality. This capability is particularly crucial in sectors such as telecommunications and data centers, where network reliability is paramount.

Another key growth driver is the increasing complexity of modern network infrastructures, especially with the rollout of 5G and beyond. As networks become more intricate, traditional monitoring solutions struggle to keep pace with the speed and scale required. AI-Enhanced Optical Line Monitoring systems leverage sophisticated algorithms to analyze vast amounts of data from multiple sources, identifying patterns and anomalies that might otherwise go undetected. This not only improves fault detection accuracy but also enhances resource allocation and network optimization. Additionally, the growing emphasis on automation and remote monitoring, driven by the need to reduce human intervention and operational expenses, is further fueling the adoption of AI-powered solutions across various industries.

Furthermore, regulatory mandates and industry standards regarding network uptime and data security are compelling organizations to invest in advanced monitoring technologies. Governments and regulatory bodies worldwide are implementing stricter guidelines to ensure the reliability and security of critical infrastructure, including telecommunications, utilities, and enterprise networks. AI-Enhanced Optical Line Monitoring solutions help organizations comply with these regulations by providing comprehensive visibility, real-time alerts, and detailed reporting capabilities. The increasing focus on sustainability and energy efficiency is also encouraging the deployment of intelligent monitoring systems that optimize network performance while minimizing energy consumption and environmental impact.

From a regional perspective, North America currently dominates the AI-Enhanced Optical Line Monitoring market, accounting for over 35% of the global market share in 2024. This leadership is driven by the presence of major technology providers, advanced telecom infrastructure, and early adoption of AI technologies. Asia Pacific is emerging as the fastest-growing region, fueled by large-scale investments in fiber optic networks, rapid urbanization, and expanding digital economies in countries such as China, India, and Japan. Europe is also witnessing significant growth, supported by government initiatives to enhance broadband connectivity and the increasing deployment of smart city projects. Latin America and the Middle East & Africa are gradually catching up, with growing investments in digital infrastructure and rising demand for high-speed connectivity.

Component Analysis

Th

Artificial Intelligence in Telecommunication Market Outlook

As per our latest research, the artificial intelligence in telecommunication market size reached USD 3.86 billion in 2024, reflecting robust adoption across global telecom operations. The market is poised for accelerated growth, with a projected CAGR of 41.2% from 2025 to 2033, and is forecasted to reach USD 88.52 billion by 2033. This remarkable expansion is primarily driven by the increasing complexity of telecom networks, surging demand for automation, and the imperative for enhanced customer experience amid rising data consumption worldwide.

One of the most significant growth factors in the artificial intelligence in telecommunication market is the exponential surge in data traffic, fueled by the proliferation of smartphones, IoT devices, and the rollout of 5G networks. Telecom operators are under mounting pressure to manage vast, dynamic networks and deliver uninterrupted connectivity. AI-powered solutions are being rapidly adopted to automate network optimization, predictive maintenance, and real-time traffic management, thus reducing downtime and operational costs. This adoption is further bolstered by the need to provide personalized services and improve customer satisfaction, as AI-driven analytics enable telecom providers to anticipate user needs, detect anomalies, and swiftly resolve issues. The integration of AI in core telecom operations is no longer optional but a strategic imperative for staying competitive in an evolving digital landscape.

Another key driver is the increasing emphasis on automation and virtualization within telecom infrastructure. As the sector migrates towards software-defined networking (SDN) and network function virtualization (NFV), AI technologies are being leveraged to orchestrate and manage virtual networks with greater efficiency and agility. This transition allows telecom operators to scale their services seamlessly, respond to fluctuating demand, and implement self-healing networks that proactively address faults before they impact end-users. Additionally, AI-enabled security systems are playing a crucial role in combating sophisticated cyber threats, safeguarding sensitive data, and ensuring regulatory compliance. The convergence of AI with emerging technologies such as edge computing and the Internet of Things is further amplifying the value proposition, driving continuous innovation in service delivery and network management.

The artificial intelligence in telecommunication market is also benefiting from robust investments in research and development, as well as strategic collaborations between telecom operators, technology vendors, and AI solution providers. These partnerships are accelerating the deployment of cutting-edge AI applications, from virtual assistants and intelligent customer support to advanced fraud detection and revenue assurance systems. Governments and regulatory bodies, particularly in Asia Pacific and North America, are supporting AI adoption through favorable policies and funding initiatives. The growing ecosystem of AI startups and the availability of scalable cloud infrastructure are lowering barriers to entry, enabling both established players and new entrants to capitalize on emerging opportunities. However, the market's rapid evolution also necessitates continuous upskilling of the telecom workforce and the development of robust governance frameworks to address ethical and privacy concerns.

Regionally, North America continues to lead the artificial intelligence in telecommunication market, accounting for the largest share in 2024, driven by early adoption of advanced technologies, significant R&D investments, and the presence of major AI and telecom companies. Europe follows closely, with a strong focus on digital transformation and regulatory support for AI integration. Asia Pacific is emerging as the fastest-growing region, propelled by large-scale 5G deployments, the expansion of smart cities, and a burgeoning digital economy in countries such as China, India, and Japan. Meanwhile, the Middle East & Africa and Latin America are witnessing steady growth, supported by increasing mobile penetration and government initiatives to modernize telecom infrastructure. This global momentum underscores the pivotal role of AI in shaping the future of telecommunications, as operators worldwide strive to deliver intelligent, resilient, and customer-centric networks.

Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. 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.

AI-Driven EV Charger Site Selection Market Outlook

According to our latest research, the AI-Driven EV Charger Site Selection market size reached USD 612.3 million globally in 2024, reflecting the accelerating adoption of electric vehicles and the critical need for optimized charging infrastructure. The market is projected to expand at a robust CAGR of 18.7% from 2025 to 2033, reaching an estimated USD 3,062.4 million by the end of the forecast period. This remarkable growth is primarily propelled by the increasing integration of artificial intelligence in infrastructure planning, rising demand for electric vehicles, and supportive government policies fostering sustainable transportation networks.

One of the primary growth factors driving the AI-Driven EV Charger Site Selection market is the exponential increase in electric vehicle adoption worldwide. As governments and private organizations push for decarbonization and reduced dependency on fossil fuels, the need for efficient and accessible EV charging infrastructure has never been higher. AI-powered site selection tools are becoming indispensable for stakeholders aiming to maximize charger utilization and minimize installation costs. These solutions leverage large datasets, including traffic patterns, demographic trends, utility grid capacity, and land use data, to recommend optimal charger locations. The ability of AI to analyze and synthesize complex variables in real time ensures that new charging stations are strategically placed to meet current and projected demand, significantly enhancing the user experience and supporting the broader shift toward electrified transportation.

Another significant factor contributing to market expansion is the growing complexity of urban mobility ecosystems. Modern cities are increasingly relying on data-driven solutions to address congestion, pollution, and energy management challenges. The integration of AI in EV charger site selection not only optimizes network coverage but also aligns with smart city initiatives. By utilizing AI-driven analytics, municipalities and private operators can identify high-impact locations, forecast future charging needs, and ensure equitable access to charging infrastructure. This data-centric approach also enables dynamic adaptation to evolving urban layouts, infrastructure projects, and changing mobility patterns, further solidifying the role of AI in sustainable urban planning and infrastructure resilience.

Furthermore, favorable regulatory frameworks and financial incentives are accelerating the adoption of AI-driven site selection solutions. Governments across North America, Europe, and Asia Pacific are introducing mandates for EV infrastructure expansion, coupled with funding for technology-driven planning tools. These policies are encouraging utilities, automotive OEMs, and charging network operators to invest in advanced site selection technologies. Additionally, the increasing involvement of public-private partnerships in EV infrastructure rollouts is fostering innovation and scalability in the deployment of AI-powered solutions. As competition intensifies, stakeholders are prioritizing the integration of AI to gain a strategic edge, reduce operational risks, and ensure the long-term viability of their charging networks.

From a regional perspective, North America and Europe are leading the adoption of AI-Driven EV Charger Site Selection solutions, owing to mature EV markets, robust digital infrastructure, and proactive government initiatives. Asia Pacific is rapidly emerging as a high-growth region, fueled by massive investments in electric mobility and urbanization. The Middle East & Africa and Latin America are also witnessing increased activity, particularly in major urban centers and tourist destinations. Regional dynamics are shaped by varying levels of EV penetration, regulatory support, and technological readiness, creating a diverse landscape of opportunities and challenges for market participants.

Component Analysis

The

According to Cognitive Market Research, the worldwide Integrated Traffic Systems market will be USD 35.98 billion in 2024 and will expand at a compounded annual growth rate (CAGR) of 8.2% from 2024 to 2031. Market Dynamics of Integrated Traffic Systems Market Key Drivers for Integrated Traffic Systems Market Increase in traffic congestion around the globe - Companies are working with government agencies to develop cutting-edge vehicle technologies as traffic congestion gets worse. For example, the USDOT supports the project to enhance connected car technology. The New York City Pilot projects a partnership between USDOT and the NYC DOT, sought to increase pedestrian and traveler safety in the city by implementing V2V and V2I-connected automobiles. This goal is in complete accordance with the Vision Zero strategy of New York City. The program aims to reduce environmental consequences, increase safety, and boost traveler movement and system efficiency by creatively and economically fusing mobile devices and linked vehicle technology. Major other nations promote public-private partnerships worldwide. The industry is anticipated to have profitable growth potential from the development of connected and autonomous vehicles, as well as from advancements in communications and high-speed internet technology. In addition to creating lucrative growth opportunities for businesses operating in the integrated traffic systems market. Key Restraints for Integrated Traffic Systems Market Coordination of the efforts of multiple government organizations, departments, and parties involved in transport administration and planning may prove difficult. Competing objectives, organizational silos, and jurisdictional barriers can all hinder the implementation of integrated transportation systems. Considerations like expensive technology costs limit the market's potential expansion. The real-time data sharing that an integrated traffic system uses to function requires complex hardware and ideal software collaboration. Introduction of the Integrated Traffic Systems Market Using cutting-edge technology like artificial intelligence (AI), the Internet of Things (IoT), and real-time data analytics, integrated traffic systems provide comprehensive solutions that maximize traffic flow, improve safety, and reduce congestion. The foreseeable future of city transportation is being shaped by the growing need for integrated transportation systems as cities place a higher priority on efficient mobility. It is motivated by growing worries about gridlock and traffic. There is an increasing need to handle traffic management difficulties as urban populations rise and highways become more congested. The market for integrated traffic systems is expanding as a result of higher government funding for these kinds of initiatives. Governments everywhere are giving infrastructure upgrades top priority because traffic congestion is being made worse by urbanization and population increase. By utilizing IoT and AI technologies, integrated traffic systems investments seek to improve traffic flow and lessen congestion. This market sector is growing as a result of the growing need for integrated highways brought on by governments spending large sums of money to update transportation networks.

Summary

Upwork (UWPK) is a leading online talent marketplace. The business experienced explosive growth over the course of the COVID-19 pandemic, but in 2023 began a pivot to profitability that has quickly and significantly improved the company’s margins. Concerns about the long-term impacts of artificial intelligence have pressured valuation and created an attractive entry point for a business that has the potential to double adjusted EBITDA by 2028.

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Business Description

UPWK operates a leading marketplace that connects companies with global talent. The company primarily facilitates offshoring of work in categories including software development, design, and writing; nearly ~70% of gross services value (“GSV”) enabled through the platform in fiscal 2023 originated from US clients, while just ~26% of GSV was completed by US-based talent (source: 2023 10-K). A typical UPWK project might span several days to a few weeks—larger than project or catalog-based marketplaces like Fiverr and 99Designs, but shorter than mandates given to traditional staffing firms. It’s our understanding that around half of UPWK traffic arrives at the site organically.

Like many marketplace models, UPWK generates fees from both client job posters (5-10% take rate, or higher for large enterprise) and talent (10% flat take rate), which together conspired to facilitate a ~17% average marketplace take rate as a percentage of GSV over the last 12 months (source: company website and quarterly filings for the LTM period ending Sept 30, 2024).

UPWK’s long tail of customers are small, but loyal. Based on 3Q24 LTM revenue, GSV, and client count disclosures in company filings and presentations, we estimate that the average disclosed marketplace client completes just ~$4,400 of marketplace GSV and generates just under ~$800 worth of revenue for UPWK. Importantly, despite the project-based nature of UPWK marketplace work, client spend has proven quite sticky from year to year, particularly for client cohorts who signed up before the pandemic.

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Client Spend by Annual Client Cohort

Source: UPWK 2023 10-K

UPWK’s Recent History

UPWK experienced explosive growth over the course of the COVID-19 pandemic, facilitated by both tight labor market conditions and increasing talent interest in remote work options. Marketplace GSV rose more than 20% in 2020 and over 40% in 2021, supported by both new clients and an increase in spend per client (source: UPWK quarterly filings). More recently, however spending on the UPWK platform has stalled as labor market conditions have loosened and undoubtedly some categories—eg. design-related work—have been impacted by increasingly proficient artificial intelligence (“AI”) models.

The potential for AI to obviate the need to send work to lower cost, more flexible jurisdictions is the bear case for UPWK shares, and we believe, the primary motivator for the company’s depressed multiple relative to potential earnings power. We note that software development/IT, writing & translation, and design & creative rank among the most posted jobs on the UPWK talent marketplace, and much of the work in these areas has the potential to be greatly aided or displaced by AI-powered tools (source: company website). At the same time, AI has had seemingly little negative impact to date: flagship AI model ChatGPT was released more than two years ago, and based on the company’s client cohort disclosure, as shown above, we think recent weakness has primarily emanated from a moderation in spend amongst the very large cohorts that joined UPWK during the pandemic when labor availability was exceptionally tight. It does not seem evident that AI/ChatGPT are having a material impact on UPWK’s business. Going forward, we think secular growth in offshoring in durable verticals (eg. finance, marketing) and growth in AI-proficient experts could help to offset declines in more exposed verticals like translation, driving a potentially more stable top line than the stock is discounting.

While a reacceleration in GSV growth would be helpful to our thesis, we do not expect a return to the halcyon days of double-digit growth. We think for the stock to work, UPWK’s marketplace simply needs to remain vibrant and around today’s size as the company executes against a profitability agenda.

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Closing the loop on marketplace revenue, we note that take marketplace take rates were stable in the ~13% context for years before a talent-side pricing change in 2023 (source: 2023 10-K). Going forward, we expect more limited improvement in take rates from more recent ~18% levels (source: 3Q24 investor presentation), but see potential upside over the intermediate-to-long term from efforts like advertising and subscriptions (which UPWK includes in take rate).

UPWK GSV

Source: UPWK quarterly and annual filings

UPWK Active Clients and GSV per Client

Source: UPWK quarterly and annual filings

Separate and apart from the talent marketplace, we note that UPWK generates a relatively small share of sales from enterprise relationships with additional features, as well as outcome-based managed services.

UPWK’s Strategic Pivot to Profitability

Like many high-growth companies, UPWK scaled investments in R&D, product, and sales during this exciting period. As growth slowed, however, we think the core talent marketplace’s underlying margin potential was obfuscated by the trappings of pandemic-era excitement. Recent headcount reductions support this view, and management clearly believes in UPWK’s margin potential, as evidenced by their 35% adj. EBITDA margin target (source: UPWK 3Q24 earnings call).

UPWK Adj. EBITDA Margin (%)

Source: UPWK quarterly and annual filings

Summarizing the Investment Thesis

To summarize, with shares hovering around $16 or ~9.5x EBITDA, we think investors are overly distracted by AI risks that haven’t discernibly materialized in top-line growth to date, and are therefore missing a potential doubling of adj. EBITDA by 2028 as the company executes against its margin self-help story. Assuming modest top-line growth and margins scaling to 35%+, adj. EBITDA could surpass $300MM by 2028 for a business with a current enterprise value of just $1.9Bn. We believe management is both incentivized to pursue this transition, and, as evidenced by results over the last two years, highly capable of doing so.

Risks

• AI: Many UPWK projects are for writing, design, coding, or other work that could be displaced in whole or part by AI-powered tools
• Competition: UPWK competes with both project-based sites like 99Designs/Fiverr, as well as more traditional staffing firms
• Employment markets: During softening job markets, employers typically first reduce spend on contract or project-based work before right-sizing their full-time labor force

Between June 2022 and March 2023, the traffic volume for the keyword "AI" has tripled, going from around 7.9 million monthly searches to more than 30.4 million during the last month of the measured period. General interest in artificial intelligence (AI) has exploded in markets like the United States by the end of 2022. Likewise, interest for the application programming interfaces (API's) and plugins of artificial intelligence solutions, especially those of ChatGPT, has also seen a major increase since the release of the tool in November of 2022.

The artificial intelligence market

Valued at around 142.3 billion U.S. dollars in 2022, the artificial intelligence market is one the most promising tech segments for the rest of the decade, with more than five billion U.S. dollars invested in startups - the most notable being the Californian company OpenAI and its flagship application ChatGPT. Disruptive as it is, the adoption of AI has already sparked an alert for several industries, likely to affect job markets and thus raising concerns about cybercrime and other online misdeeds.

The future of online search?

Of most industries, the impact of the new tool developed by OpenAI may be felt by the online search market like a global earthquake. With chatbots providing search results in a dialogue format, the trend of AI-powered search engines unleashed by ChatGPT threw giant companies like Google and Microsoft into a race with startups and other competitors to present the best candidate for this disruptive (and experimental) online solution.

The size of the Deep Packet Inspection Market was valued at USD 4.76 Billion in 2023 and is projected to reach USD 16.09 Billion by 2032, with an expected CAGR of 19.00% during the forecast period. The Deep Packet Inspection (DPI) market is experiencing significant growth due to the increasing demand for advanced network security solutions and data optimization. DPI technology enables detailed examination of data packets passing through a network, offering critical insights into traffic patterns, security threats, and user behaviors. This has made it a vital tool for industries such as telecommunications, government, and IT, where cybersecurity and efficient data management are paramount. The rising adoption of cloud-based services, the surge in cyberattacks, and stringent regulatory requirements are driving the adoption of DPI solutions globally. Additionally, advancements in artificial intelligence and machine learning are enhancing DPI capabilities, allowing for real-time threat detection and automated responses. Market players are focusing on innovative product development and strategic collaborations to strengthen their positions. However, challenges such as data privacy concerns and the high cost of implementation could limit growth in certain regions. Despite this, the DPI market is poised for sustained expansion, with its applications in network optimization, content filtering, and lawful interception remaining key drivers of demand. Key drivers for this market are: Increased cyber threats Remote work and cloud adoption Growing network traffic AI/ML integration Regulatory compliance. Potential restraints include: High implementation and maintenance costs Concerns about data privacy and security Performance impact on network bandwidth. Notable trends are: Rising Cyber Threats: Escalating cyberattacks, such as ransomware and DDoS, drive the need for advanced threat detection capabilities. Remote Work and Cloud Adoption: The shift towards remote work and cloud-based services increases the attack surface, necessitating Deep Packet Inspection solutions. Increased Network Traffic: The proliferation of mobile devices and IoT devices has led to an exponential growth in network traffic, requiring deeper packet inspection to identify and mitigate threats. AI/ML Integration: The incorporation of AI/ML algorithms enhances the effectiveness and efficiency of Deep Packet Inspection, reducing false positives and improving threat detection. Managed Services: The growing popularity of managed services allows organizations to outsource their security operations, including Deep Packet Inspection, to experienced providers..

In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.