Advertising Video on Demand (AVOD)

How Advertising Video on Demand AVOD Works

+---------------------+      +----------------------+      +---------------------+
|   User Request      | →    |   Ad Decisioning     | →    |    Ad Insertion     |
+---------------------+      +----------------------+      +---------------------+
           │                            │                            │
           ↓                            ↓                            ↓
+---------------------+      +----------------------+      +---------------------+
|  Initial Traffic    |      |  Real-Time Analysis  |      |   Content Delivery  |
|    Validation       |      | (Behavioral/IP)      |      | (with Verified Ads) |
+---------------------+      +----------------------+      +---------------------+
           │                            │                            │
           └───────────→    |   Fraud Scoring      |      ←────────────┘
                            +----------------------+
                                         │
                                         ↓
                               +---------------------+
                               |   Blocking/Alerting  |
                               +---------------------+

In an Advertising Video on Demand (AVOD) model, the system is designed to deliver advertisements to viewers while simultaneously protecting advertisers from fraud. The process involves multiple stages that validate traffic and ensure ad interactions are legitimate before, during, and after content delivery. This intricate workflow is crucial for maintaining the integrity of advertising campaigns and ensuring a return on investment.

Initial Request and Validation

When a user initiates a video stream on an AVOD platform, the first step is a basic validation of the incoming request. This involves checking the user agent, IP address, and other header information for any immediate signs of non-human traffic. For example, requests from known data center IPs or those with outdated user agents are often flagged at this early stage. This initial screening acts as a first line of defense, filtering out the most obvious bot traffic before it consumes further resources.

Real-Time Ad Decisioning and Analysis

Once the initial request is deemed plausible, it proceeds to the ad decisioning engine. Here, the system selects an appropriate ad to show the user based on targeting criteria. Simultaneously, a deeper, real-time analysis of the user’s session begins. This involves behavioral analysis, such as mouse movements and interaction patterns, and cross-referencing the IP address against fraud databases. The goal is to build a profile of the user to determine if their behavior aligns with that of a typical human viewer.

Fraud Scoring and Mitigation

Based on the data collected, the system assigns a fraud score to the session. This score is a calculated risk assessment that determines the likelihood of the traffic being fraudulent. If the score exceeds a predefined threshold, the system can take several actions. It might block the ad from being served altogether, serve a public service announcement instead of a paid ad, or simply flag the interaction for later review. This scoring and mitigation process is continuous, adapting to new patterns and threats as they emerge.

Diagram Element Breakdown

User Request & Initial Validation

This represents the start of the user’s journey. The system performs preliminary checks on the request source, looking for obvious red flags associated with fraudulent origins like data centers or proxies.

Ad Decisioning & Real-Time Analysis

At this stage, an ad is selected while the system analyzes user behavior in real-time. It scrutinizes patterns to distinguish between human engagement and automated scripts, which is critical for preventing sophisticated bot fraud.

Ad Insertion & Content Delivery

This is where the selected ad is stitched into the video stream. For traffic deemed legitimate, the ad is delivered seamlessly. This step relies on the accuracy of the previous analysis to ensure advertisers only pay for valid impressions.

Fraud Scoring & Blocking/Alerting

This component aggregates all collected data points to generate a risk score. High-risk traffic is then blocked, or an alert is sent to administrators. This is the core of the proactive defense, preventing financial loss and protecting campaign data.

🧠 Core Detection Logic

Example 1: Session Velocity Analysis

This logic tracks the frequency of ad requests from a single user session or IP address within a specific timeframe. An abnormally high number of requests can indicate a bot programmed to generate impressions rapidly. This is a crucial heuristic for identifying non-human traffic patterns that deviate from normal viewing behavior.

FUNCTION check_session_velocity(session_id, time_window, max_requests):
  request_log = get_requests_for_session(session_id, time_window)
  
  IF count(request_log) > max_requests:
    FLAG_AS_FRAUD(session_id, "High Request Velocity")
    RETURN True
  
  RETURN False

Example 2: Geographic Mismatch Detection

This technique compares the user’s declared geographic location (e.g., from their profile) with the location derived from their IP address. A significant mismatch can suggest the use of a VPN or proxy to mask the user’s true origin, a common tactic in ad fraud to mimic high-value traffic.

FUNCTION check_geo_mismatch(user_profile_country, ip_address):
  ip_country = get_country_from_ip(ip_address)
  
  IF user_profile_country IS NOT ip_country:
    FLAG_AS_SUSPICIOUS(ip_address, "Geographic Mismatch")
    RETURN True
  
  RETURN False

Example 3: Viewer Interaction Analysis

This logic analyzes in-stream user behavior, such as mouse movements, click patterns, and video player engagement (e.g., pausing, rewinding). Bots often exhibit predictable, non-human patterns, like no mouse movement or clicking in the exact same spot on every ad. Deviations from organic human interaction are flagged as suspicious.

FUNCTION analyze_viewer_interaction(session_data):
  mouse_events = session_data.mouse_events
  click_coordinates = session_data.click_coordinates
  
  IF count(mouse_events) < MIN_EXPECTED_MOVEMENTS:
    FLAG_AS_FRAUD(session_data.id, "Lack of Mouse Activity")
    RETURN True
  
  IF has_repetitive_click_pattern(click_coordinates):
    FLAG_AS_FRAUD(session_data.id, "Repetitive Click Pattern")
    RETURN True
  
  RETURN False

📈 Practical Use Cases for Businesses

• Campaign Budget Protection: Prevents ad spend from being wasted on fraudulent impressions generated by bots, ensuring that budgets are spent on reaching real, potential customers.
• Data Integrity for Analytics: Ensures that marketing analytics and performance metrics are based on genuine user interactions, leading to more accurate insights and better strategic decisions.
• Improved Return on Ad Spend (ROAS): By filtering out invalid traffic, AVOD fraud protection ensures that ads are shown to a receptive audience, thereby increasing the likelihood of conversions and improving overall ROAS.
• Brand Safety Maintenance: Protects brand reputation by preventing ads from being displayed in fraudulent or inappropriate contexts, which can be a side effect of certain ad fraud schemes.

Example 1: IP Blocklist Rule

This pseudocode demonstrates a basic IP filtering rule. A business can maintain a dynamic blocklist of IP addresses that have been identified as sources of fraudulent activity. Before an ad is served, the system checks the user's IP against this list.

FUNCTION should_serve_ad(user_ip):
  ip_blocklist = get_global_ip_blocklist()
  
  IF user_ip IN ip_blocklist:
    RETURN False  // Do not serve the ad
  
  RETURN True  // Serve the ad

Example 2: Session Score for Conversion Funnels

This logic scores a user's session based on various fraud indicators. Businesses can use this score to decide whether to trust a conversion event (e.g., a lead submission). A session with a high fraud score might have its conversion event flagged or disregarded to maintain clean performance data.

FUNCTION calculate_session_fraud_score(session_data):
  score = 0
  
  IF is_from_datacenter(session_data.ip):
    score += 40
  
  IF has_no_mouse_movement(session_data.events):
    score += 30
    
  IF session_data.request_frequency > HIGH_THRESHOLD:
    score += 30
    
  RETURN score

// Business Logic
session_score = calculate_session_fraud_score(current_session)
IF session_score < 70:
  process_conversion_event(current_session)
ELSE:
  flag_conversion_as_suspicious(current_session)

🐍 Python Code Examples

This Python function checks if a given IP address belongs to a known data center, which is often a source of non-human traffic. This helps in filtering out bot-generated impressions at the entry point of the ad-serving process.

# Example list of known data center IP ranges
DATACENTER_IP_RANGES = [
    "192.168.0.0/16",
    "10.0.0.0/8" 
]

def is_datacenter_ip(ip_address):
    """Checks if an IP address falls within known data center ranges."""
    from ipaddress import ip_address as ip_addr, ip_network
    
    try:
        incoming_ip = ip_addr(ip_address)
        for ip_range in DATACENTER_IP_RANGES:
            if incoming_ip in ip_network(ip_range):
                return True
    except ValueError:
        return False  # Invalid IP address format
    return False

# --- Usage ---
# print(is_datacenter_ip("10.1.2.3")) # Returns True

This code analyzes a list of timestamps from a single user session to detect abnormally high click frequency. By calculating the time difference between consecutive clicks, it can identify automated scripts designed to generate fraudulent clicks.

def has_abnormal_click_frequency(timestamps, threshold_seconds=1):
    """Detects if clicks are occurring faster than a humanly possible rate."""
    if len(timestamps) < 2:
        return False
    
    for i in range(1, len(timestamps)):
        time_diff = timestamps[i] - timestamps[i-1]
        if time_diff.total_seconds() < threshold_seconds:
            return True # Flag as suspicious
            
    return False

# --- Usage ---
# from datetime import datetime, timedelta
# clicks = [datetime.now(), datetime.now() + timedelta(milliseconds=100)]
# print(has_abnormal_click_frequency(clicks)) # Returns True

Types of Advertising Video on Demand AVOD

• Server-Side Ad Insertion (SSAI): Ads are stitched directly into the video content on the server before it reaches the user's device. This method makes it harder for ad blockers to stop the ads and provides a smoother viewing experience, but can also be exploited by sophisticated bots that mimic legitimate server calls.
• Client-Side Ad Insertion (CSAI): The user's device (the client) requests ads from an ad server separately from the video content. This is more common but easier for ad blockers to intercept. From a fraud perspective, it allows for more direct measurement of client-side signals like mouse movement and visibility.
• Hybrid AVOD-SVOD Models: Platforms offer a tiered subscription model, where a free or low-cost tier is supported by ads (AVOD), while a premium tier is ad-free (SVOD). Fraud detection in this model focuses on the AVOD tier, where invalid traffic directly impacts advertising revenue.
• Contextual Ad Targeting: Ads are served based on the content of the video being watched rather than on user data. In fraud detection, this involves verifying that ad placements are relevant and that bots are not generating views on high-value content to attract expensive ads fraudulently.

🛡️ Common Detection Techniques

• IP Reputation Analysis: This technique involves checking the user's IP address against databases of known fraudulent sources, such as data centers, proxies, and VPNs. It's a fundamental first step in filtering out traffic that is likely non-human or attempting to hide its origin.
• Behavioral Analysis: The system analyzes user interaction patterns, like mouse movements, click rates, and viewing duration. Bots often exhibit predictable or unnatural behaviors, such as no mouse activity or impossibly fast clicks, which this technique helps to identify.
• Device Fingerprinting: This method collects various attributes from a user's device (e.g., browser type, operating system, screen resolution) to create a unique identifier. This helps detect when a single entity is attempting to mimic multiple users by running scripts across different virtual devices.
• Ad Stacking and Pixel Stuffing Detection: This technique inspects the rendering of a webpage to ensure that multiple ads are not being stacked on top of each other in a single ad slot (ad stacking) or that ads are not being displayed in tiny 1x1 pixels (pixel stuffing). Both are methods used to generate fraudulent impressions.
• Session Velocity Monitoring: The system tracks the number of ad requests or clicks coming from a single session or IP address over a short period. An unusually high velocity often indicates automated bot activity designed to generate a large volume of impressions quickly.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
TrafficGuard	Specializes in preemptive ad fraud prevention, analyzing click paths and user behavior to block invalid traffic before it impacts campaigns. Particularly effective for mobile and CTV environments.	Proactive blocking, strong in mobile fraud detection, improves conversion rates by eliminating waste.	Can be more expensive, may require technical integration for full effectiveness.
ClickCease (CHEQ Essentials)	Offers real-time detection and automated blocking of fraudulent clicks for PPC campaigns. It uses machine learning to identify suspicious IPs and user behavior.	Easy integration with major ad platforms, real-time blocking, provides detailed analytics on blocked traffic.	Primarily focused on click fraud, may be less effective against sophisticated impression fraud in video.
HUMAN (formerly White Ops)	A cybersecurity company that specializes in modern bot protection. It verifies the humanity of more than 15 trillion interactions per week for some of the largest companies and internet platforms.	Excellent at detecting sophisticated botnets, provides collective protection based on wide network visibility, strong in CTV/OTT fraud.	Can be a high-cost enterprise solution, may be overly complex for smaller businesses.
Integral Ad Science (IAS)	Provides ad verification solutions that measure media quality, including brand safety, viewability, and ad fraud. It analyzes impressions and clicks to ensure they are legitimate.	Comprehensive media quality metrics, trusted by major brands and agencies, strong reporting capabilities.	Can be costly, often bundled with other services which may not be needed by all advertisers.

📊 KPI & Metrics

Tracking both technical accuracy and business outcomes is crucial when deploying fraud detection in AVOD. Technical metrics validate the system's effectiveness in identifying invalid traffic, while business metrics demonstrate the financial impact of that protection, ensuring the investment in fraud prevention delivers a tangible return.

Metric Name	Description	Business Relevance
Invalid Traffic (IVT) Rate	The percentage of ad traffic identified as fraudulent or non-human.	Indicates the overall exposure to fraud and the effectiveness of filtering efforts.
False Positive Rate	The percentage of legitimate user interactions incorrectly flagged as fraudulent.	A high rate can lead to blocking real customers and losing potential revenue.
Cost Per Mille (CPM) on Valid Traffic	The effective cost per thousand impressions after fraudulent views are removed.	Measures the true cost of reaching a genuine audience, impacting budget allocation.
View-Through Conversion Rate	The percentage of users who convert after seeing an ad, based on verified, viewable impressions.	Helps to accurately assess campaign effectiveness and return on ad spend (ROAS).

These metrics are typically monitored through real-time dashboards provided by the fraud detection platform or ad server. Automated alerts are often configured to notify teams of sudden spikes in invalid traffic or unusual patterns. This feedback loop is essential for continuously tuning fraud filters and adapting the detection rules to counter evolving threats and optimize campaign performance.

🆚 Comparison with Other Detection Methods

Real-Time vs. Post-Campaign Analysis

AVOD fraud detection heavily relies on real-time analysis to block invalid traffic before an ad is served or an impression is counted. This is more effective at preventing budget waste than post-campaign analysis, which identifies fraud after the money has already been spent. While post-campaign analysis is useful for clawbacks and blacklisting, real-time prevention offers immediate financial protection.

Behavioral Analysis vs. Signature-Based Filtering

Signature-based filtering uses known patterns of fraud (like specific bot user agents or IP addresses) to block threats. While fast, it is ineffective against new or unknown threats. AVOD systems increasingly use behavioral analysis, which focuses on how a user interacts with content. This is more adaptable and effective against sophisticated bots that can mimic human signatures but fail to replicate genuine human behavior.

Heuristics vs. CAPTCHAs

CAPTCHAs are challenges designed to differentiate humans from bots. However, they are intrusive and disrupt the seamless viewing experience expected in AVOD. Heuristic-based detection, which operates in the background analyzing data points like session velocity and geographic consistency, is non-intrusive. It protects the user experience while effectively scoring traffic, making it far more suitable for video advertising environments.

⚠️ Limitations & Drawbacks

While crucial for protecting ad spend, fraud detection within Advertising Video on Demand (AVOD) is not without its challenges. These systems can be resource-intensive, may struggle with new fraud techniques, and can sometimes misidentify legitimate users, leading to potential issues in campaign measurement and user experience.

• False Positives: Overly aggressive detection rules may incorrectly flag legitimate users as fraudulent, potentially blocking real customers and leading to lost revenue opportunities.
• Latency Issues: Real-time analysis and ad insertion can introduce slight delays (latency) in video playback, which may negatively impact the user experience if not properly optimized.
• Evolving Fraud Tactics: Fraudsters constantly develop new methods, meaning detection systems must be continuously updated. A system may be temporarily vulnerable to novel attacks it has not yet learned to identify.
• Encrypted Traffic and Privacy: Increasing privacy regulations and the use of encrypted traffic can limit the data available for analysis, making it more difficult to accurately distinguish between human and non-human behavior.
• Sophisticated Bot Mimicry: Advanced bots can mimic human-like mouse movements and interaction patterns, making them difficult to distinguish from real users based on behavioral analysis alone.

In scenarios where traffic is highly variable or new fraud schemes are emerging rapidly, a hybrid approach combining multiple detection methods may be more suitable to balance accuracy and performance.

❓ Frequently Asked Questions

How does AVOD fraud affect advertisers?

AVOD fraud directly impacts advertisers by wasting their ad spend on fake impressions and clicks generated by bots. This leads to skewed performance metrics, inaccurate analytics, and a lower return on ad spend (ROAS), as the ads are not being seen by genuine potential customers.

Can Server-Side Ad Insertion (SSAI) eliminate ad fraud?

While Server-Side Ad Insertion (SSAI) can prevent many client-side fraud techniques and ad blocking, it does not eliminate ad fraud entirely. Sophisticated bots can still generate fraudulent requests at the server level, making it essential to have robust detection that analyzes traffic patterns before the ad is stitched into the stream.

Is a high volume of traffic from a single IP always fraud?

Not necessarily. A high volume of traffic from a single IP could originate from a large corporate network or a university, where many legitimate users share the same public IP address. Fraud detection systems must use additional signals, such as user-agent diversity and behavioral patterns, to distinguish this from fraudulent bot traffic.

What is the difference between general invalid traffic (GIVT) and sophisticated invalid traffic (SIVT)?

General Invalid Traffic (GIVT) refers to easily identifiable non-human traffic, such as bots and crawlers from known data centers. Sophisticated Invalid Traffic (SIVT) is more advanced and includes hijacked devices, ad stacking, and bots designed to mimic human behavior, requiring more complex detection methods to identify.

How does fraud in AVOD differ from fraud in display advertising?

While both share common fraud types like bots and fake clicks, AVOD fraud has unique challenges related to video metrics. These include verifying that the video ad was actually viewable and played for a sufficient duration, as well as dealing with fraud specific to Connected TV (CTV) environments, which often lack the cookies and standard identifiers present in web browsers.

🧾 Summary

Advertising Video on Demand (AVOD) provides free, ad-supported content to viewers. Within ad fraud protection, its core function is to ensure that advertisements are served to real people, not bots. By analyzing traffic in real-time and identifying non-human behavior, AVOD fraud prevention systems protect advertising budgets, ensure data accuracy, and maintain the integrity of digital marketing campaigns.

🔗 Related Articles

• What Is AVOD (Advertising Video on Demand)? – To AVOD or avoid? - https://www.rpc.co.uk/perspectives/media-and-tech-tmt/advertising-video-on-demand-to-avod-or-avoid/
• How to Prevent Click Fraud | Built In - https://builtin.com/marketing/click-fraud
• Click Fraud Protection & Detection Software | ClickCease™ - https://www.clickcease.com/
• How to prevent and detect fraudulent activities of ad clicks & ad views? | AdSpeed AdServer - https://www.adspeed.com/articles/ad-fraud-prevention-detection.html
• AVOD (Advertising-Based Video On Demand) - AppsFlyer - https://www.appsflyer.com/glossary/avod-advertising-based-video-on-demand/