Page views are not a reliable indication of human eyes reading a webpage because a significant portion of page views can be generated by non-human traffic sources like bots and automated programs. Here are some key reasons why page views alone do not accurately reflect genuine human readership:
- Search engine bots: Search engines like Google use web crawlers or bots to index and rank web pages. These bots visit and load pages, contributing to page view counts without any human actually reading the content.
- Spambots: Malicious bots are programmed to visit websites and perform activities like posting spam comments, scraping content, or attempting credential stuffing attacks. These spambots inflate page view metrics without any legitimate user engagement.
- Denial of Service (DoS) attacks: Botnets can be used to launch distributed denial of service (DDoS) attacks by overwhelming a website with an extremely high volume of requests and page loads, artificially inflating page view counts.
- Click fraud bots: Some bots are designed to generate fake ad clicks or page views to drain advertising budgets or manipulate website analytics.
- Web scrapers: Bots that scrape website content by rapidly loading and extracting data from pages can contribute significantly to page view counts without any human readership.
- Impersonator bots: Sophisticated bots can mimic human browsing behavior, making it difficult to distinguish their page views from those of real users.
Using a CDN (Content Delivery Network) like Cloudflare can effectively filter out non-human traffic before it reaches the origin server through various techniques:
Cloudflare employs advanced machine learning algorithms and behavioral analysis to accurately identify and classify bot traffic based on patterns and anomalies in network requests. This allows them to distinguish between legitimate human traffic and malicious or unwanted bot activity.
- Machine Learning: Cloudflare trains its machine learning models on a vast dataset of hundreds of billions of requests per day from millions of Internet properties. This enables highly accurate bot scoring and detection.
- Behavioral Analysis: Cloudflare analyzes request patterns, fingerprints, and deviations from baseline behavior to identify bot characteristics like rapid-fire requests, headless browsers, and non-human interactions.
- Fingerprinting: Cloudflare can fingerprint bots based on various attributes like HTTP headers, JavaScript signatures, and device characteristics, making it harder for bots to evade detection by mimicking human behavior.
Once identified, Cloudflare provides powerful tools to manage and mitigate different types of bot traffic:
- Blocking: Cloudflare can block known malicious bots like those involved in credential stuffing, content scraping, inventory hoarding, and DDoS attacks before they reach the origin server.
- Rate Limiting: Cloudflare can rate-limit or throttle excessive requests from a single IP address or ASN (Autonomous System Number), preventing bot-driven traffic spikes and DDoS attacks.
- Challenge Systems: For suspicious traffic, Cloudflare can challenge potential bots with invisible challenges like JavaScript renderings or device fingerprinting, allowing legitimate human users through while blocking bots unable to pass the challenges.
- Customizable Rules: Cloudflare allows website owners to define custom rules and criteria to manage bot traffic based on their specific needs, such as allowing certain types of bots or blocking specific IP ranges.
By leveraging Cloudflare’s global network and advanced bot management capabilities, website owners can offload the burden of filtering non-human traffic from their origin servers, improving website performance, security, and analytics accuracy.
To accurately measure human readership, website owners need to implement bot detection and filtering mechanisms to identify and exclude non-human traffic from their analytics data. Metrics like time on page, scroll depth, and user interactions can provide better insights into genuine user engagement compared to raw page view counts.