Attribution window

How Attribution window Works

User Click Event              Attribution Window (e.g., 7 days)                Conversion Event
+-----------------+           +----------------------------------------------+           +----------------+
| Ad Click        |───────────│ System Monitors for Conversion             │───────────│ App Install    │
| (Timestamp 1)   |           │                                              │           | (Timestamp 2)  |
+-----------------+           │ ┌──────────────────────────────────────────┐ │           +----------------+
                              │ │ Validation Logic                         │ │
                              │ │                                          │ │
                              │ │ 1. Is (T2 - T1) within window?           │ │
                              │ │ 2. Is CTIT abnormally short? (→ Fraud?)  │ │
                              │ │ 3. Is pattern suspicious? (→ Fraud?)     │ │
                              │ └──────────────────────────────────────────┘ │
                              +----------------------------------------------+
                                   │
                                   └─► IF FRAUD: Reject Attribution
                                   └─► IF VALID: Credit Publisher

An attribution window serves as a critical timer in digital advertising to link marketing efforts to results like app installs. In fraud detection, its primary function is to create a logical timeframe where a conversion can be legitimately credited to a preceding click. Fraudulent activities often manipulate this timing, which security systems can detect by analyzing patterns within this window.

Time-Based Correlation

When a user clicks an ad, a timestamp is recorded. If that user later converts (e.g., installs the app), another timestamp is logged. The attribution window is the maximum allowed duration between these two events for the ad to get credit. A typical window might be seven days for a click. Any conversion happening outside this period is generally not attributed to the ad, often being classified as an organic conversion. This simple rule is the first line of defense against crediting unrelated events.

Click-to-Install Time (CTIT) Analysis

A core component of fraud detection is analyzing the Click-to-Install Time (CTIT), which is the precise time between the ad click and the first app open. Fraud tactics like click injection occur when a fraudster injects a click just moments before an install completes, resulting in an unnaturally short CTIT, often just a few seconds. By flagging these impossibly fast conversions, systems can reject the fraudulent attribution claim and protect ad budgets.

Pattern Recognition and Anomaly Detection

Beyond single events, security systems analyze the distribution of CTITs across a campaign. Legitimate users show a natural curve—some install quickly, others take hours. In contrast, fraud schemes like click spamming, which generate massive volumes of fake clicks, produce a flat or random CTIT distribution. Identifying these abnormal patterns within the attribution window helps systems filter out low-quality or fraudulent traffic sources that fail to show genuine user intent.

Breaking Down the Diagram

User Click Event (Timestamp 1)

This block represents the moment a user interacts with an ad. The system records a precise timestamp, which is the starting point for the attribution window. This initial data point is essential for all subsequent fraud analysis, as it establishes the “cause” in the cause-and-effect relationship being measured.

Attribution Window & Validation Logic

This central part of the diagram illustrates the monitoring period. The system doesn’t just wait for the window to end; it actively applies validation logic to any conversion that occurs. This logic checks the time difference against fraud indicators, such as abnormally short CTITs associated with click injection or dispersed patterns linked to click spamming. This is the core of the detection process.

Conversion Event (Timestamp 2)

This represents the desired user action, such as an app install or first open. Its timestamp provides the “effect.” The relationship between Timestamp 1 and Timestamp 2 is scrutinized to determine legitimacy. Based on the validation logic, the system decides whether to credit the publisher for a valid conversion or reject it as fraudulent, thereby preventing wasted ad spend.

🧠 Core Detection Logic

Example 1: Click-to-Install Time (CTIT) Anomaly Detection

This logic flags conversions that happen too quickly after a click, which is a strong indicator of click injection fraud. Click injection occurs when malware on a device detects an app installation and programmatically fires a click just before it completes to steal attribution.

// Define a minimum threshold for a realistic CTIT
MIN_CTIT_SECONDS = 10;

FUNCTION check_ctit_fraud(click_timestamp, install_timestamp):
  // Calculate the time difference in seconds
  ctit = install_timestamp - click_timestamp;

  // If the time is unnaturally short, flag it as fraud
  IF ctit < MIN_CTIT_SECONDS THEN
    RETURN "Fraudulent: Click Injection Suspected";
  ELSE
    RETURN "Valid";
  END IF
END FUNCTION

Example 2: Attribution Stacking Prevention

This logic prevents click spamming, where fraudsters send huge volumes of clicks hoping to land one within the attribution window of an organic install. It works by invalidating previous clicks from the same source if they occur too frequently without a conversion.

// Define frequency and time limits
MAX_CLICKS_PER_HOUR = 20;
SOURCE_ID = "publisher_xyz";

FUNCTION check_click_spam(source_id, click_timestamp):
  // Get recent clicks from this source
  recent_clicks = get_clicks_from(source_id, last_hour);

  // If click frequency exceeds the threshold, invalidate attribution
  IF count(recent_clicks) > MAX_CLICKS_PER_HOUR THEN
    // Reject attribution for this click and others from this source
    invalidate_attribution(click_timestamp, source_id);
    RETURN "Fraudulent: High-Frequency Clicking";
  ELSE
    RETURN "Valid";
  END IF
END FUNCTION

Example 3: Geographic Mismatch Rule

This logic detects fraud where the location of the ad click and the conversion event (e.g., app install) do not match. Such a mismatch can indicate the use of proxies, VPNs, or other methods to disguise the true origin of the traffic.

// Geolocation data for click and install events
CLICK_COUNTRY = "Vietnam";
INSTALL_COUNTRY = "USA";

FUNCTION check_geo_mismatch(click_country, install_country):
  // If the countries are different, it's a red flag
  IF click_country != install_country THEN
    // Additional checks can be performed (e.g., known VPN data centers)
    RETURN "Suspicious: Geographic Mismatch";
  ELSE
    RETURN "Valid";
  END IF
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding – Protects active advertising campaigns by using short attribution windows to invalidate fraudulent clicks from click spamming, ensuring budgets are spent on users showing genuine and recent intent.
• Data Integrity – Ensures marketing analytics are clean by filtering out fake installs and events. This leads to more accurate performance metrics like Customer Acquisition Cost (CAC) and Return on Ad Spend (ROAS).
• Vendor & Publisher Vetting – Helps businesses evaluate traffic quality from different ad networks. Consistently abnormal CTIT distributions or high fraud rates from a partner signal low-quality or fraudulent sources to be blocked.
• Organic Poaching Prevention – Prevents fraudsters from stealing credit for organic users. By enforcing a reasonable attribution window, it ensures that only clicks that genuinely influenced a user's decision get attributed.

Example 1: CTIT Distribution Monitoring Rule

This logic helps businesses identify low-quality ad networks by analyzing the overall pattern of conversion times. A healthy network will show a natural curve, while a fraudulent one often shows a flat, dispersed distribution, indicating random clicks rather than genuine user engagement.

// Pseudocode for analyzing a publisher's CTIT data
FUNCTION analyze_publisher_ctit(publisher_id, time_period):
  // Get all conversion times for the publisher in the last 24 hours
  ctit_data = get_ctit_for_publisher(publisher_id, time_period);

  // Calculate the standard deviation of the CTIT data
  stdev_ctit = calculate_stdev(ctit_data);

  // A very high standard deviation suggests a flat, random distribution (fraud)
  IF stdev_ctit > THRESHOLD_HIGH_STDEV THEN
    RETURN "Action: Review Publisher - Suspected Click Spamming";
  ELSE
    RETURN "Status: Healthy Traffic Pattern";
  END IF
END FUNCTION

Example 2: New Device Fraud Rule

This logic identifies install farm activity, where fraudsters use new or reset device IDs for each fake install. By checking if a device has prior history, a business can flag installs that are highly likely to be non-human and part of a coordinated fraud scheme.

// Pseudocode to check for new device anomalies
FUNCTION check_new_device_fraud(device_id, install_timestamp):
  // Check for any previous activity from this device ID
  device_history = get_activity_for_device(device_id);

  // No history might be normal, but if clustered with other new devices from the same IP, it's suspicious
  IF is_empty(device_history) THEN
    ip_address = get_ip_for_install(install_timestamp);
    new_device_installs_from_ip = count_new_device_installs(ip_address, last_hour);

    IF new_device_installs_from_ip > NEW_DEVICE_THRESHOLD THEN
      RETURN "Fraud Alert: Potential Install Farm Activity from IP";
    END IF
  END IF

  RETURN "Status: Normal";
END FUNCTION

🐍 Python Code Examples

This function simulates checking the time between a click and an app install. It helps detect click injection fraud, where a fraudulent click is fired just seconds before an organic install completes to steal attribution.

import time

def check_click_injection(click_timestamp, install_timestamp, min_threshold_seconds=10):
    """
    Flags an install as potentially fraudulent if the time between click
    and install (CTIT) is unnaturally short.
    """
    ctit = install_timestamp - click_timestamp
    if ctit < min_threshold_seconds:
        print(f"FRAUD DETECTED: CTIT of {ctit:.2f}s is below the threshold of {min_threshold_seconds}s.")
        return True
    else:
        print(f"VALID: CTIT of {ctit:.2f}s is normal.")
        return False

# Example Usage:
click_time = time.time()
time.sleep(2) # Simulate a 2-second delay for a fraudulent install
install_time = time.time()
check_click_injection(click_time, install_time)

time.sleep(60) # Simulate a 60-second delay for a legitimate install
install_time_2 = time.time()
check_click_injection(click_time, install_time_2)

This script demonstrates how to identify click spamming from a specific IP address. It counts the number of clicks from an IP within a short timeframe and flags it if the count exceeds a reasonable limit, a common pattern in bot-driven fraud.

def is_click_spam(ip_address, click_logs, max_clicks=15, window_seconds=60):
    """
    Detects click spam by checking if an IP has an excessive number
    of clicks within a given time window.
    """
    current_time = time.time()
    recent_clicks = [
        log for log in click_logs
        if log['ip'] == ip_address and (current_time - log['timestamp']) < window_seconds
    ]

    if len(recent_clicks) > max_clicks:
        print(f"FRAUD DETECTED: IP {ip_address} has {len(recent_clicks)} clicks in the last minute.")
        return True
    else:
        print(f"VALID: IP {ip_address} has normal click frequency.")
        return False

# Example Usage:
# A log of recent clicks (timestamp, ip)
click_log_data = [
    {'timestamp': time.time() - i, 'ip': '123.45.67.89'} for i in range(20)
]
click_log_data.append({'timestamp': time.time(), 'ip': '98.76.54.32'})

is_click_spam('123.45.67.89', click_log_data)
is_click_spam('98.76.54.32', click_log_data)

Types of Attribution window

• Click-Through Attribution Window – This is the most common type, defining the period after a user clicks an ad during which an install can be credited. It is highly effective for measuring direct response, and using shorter windows (e.g., 24 hours vs. 7 days) helps prevent fraud like click spamming from claiming credit for organic installs.
• View-Through Attribution Window – This measures conversions that happen after a user sees an ad but does not click. The window is typically much shorter (e.g., 1-24 hours) because the user's intent is less explicit. It is more susceptible to fraud as impressions are easier to fake than clicks.
• Reattribution Window – This is used to credit a new marketing campaign for re-engaging an inactive or lapsed user. In fraud prevention, it helps distinguish between a genuine re-engagement and fraudulent attempts to claim credit for a user who was already active, ensuring budgets are spent on winning back users, not on fake activity.
• Configurable Attribution Window – This allows advertisers to dynamically set window lengths based on the campaign, channel, or known fraud patterns. For example, a shorter window can be set for a network known for high levels of click spam to minimize the risk of fraudulent attributions.

🛡️ Common Detection Techniques

• Click-to-Install Time (CTIT) Analysis – This technique measures the time between an ad click and the first app open. Abnormally short times (e.g., under 10 seconds) indicate click injection, while a flat, widely dispersed distribution of times can reveal click spamming.
• IP Address Monitoring – This involves tracking the IP addresses associated with clicks and conversions. A high volume of clicks from a single IP address or clicks from known data center proxies are flagged as suspicious, helping to identify botnets or click farms.
• Device Fingerprinting – This technique analyzes a combination of device attributes (OS, model, settings) to identify unique users. It helps detect install farms where fraudsters use emulators or real devices but reset their advertising ID for each fake install to appear as a new user.
• Behavioral Analysis – This method examines post-install user behavior. If a large cohort of users attributed to a specific source shows no meaningful engagement after an install, it suggests the installs were fraudulent and generated solely for the payout, not by genuine users.
• Geographic Mismatch Detection – This technique compares the location of the click with the location of the install or subsequent user activity. A significant mismatch, such as a click from one country and an install from another, indicates the use of VPNs or other masking techniques to hide fraudulent activity.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
TrafficGuard	Offers real-time ad fraud prevention across multiple channels, including PPC and mobile. It uses multi-layered detection to identify and block both general and sophisticated invalid traffic (GIVT and SIVT).	Proactive prevention mode, detailed reporting, and broad multi-platform support (Google, Facebook, etc.).	Can be complex for beginners due to the depth of features and data provided.
ClickCease	An automated click fraud detection and blocking service that integrates with major ad platforms like Google Ads and Facebook. It uses proprietary algorithms and offers features like competitor IP exclusion.	Real-time blocking, session recordings to analyze behavior, and industry-specific detection settings.	The number of IPs that can be blocked on some platforms (like Google Ads) is limited, which may be a constraint for large-scale attacks.
Spider AF	A click fraud protection tool that focuses on detecting invalid traffic from bots and bad actors. It scans device and session-level metrics to identify signs of automated behavior and protect ad spend.	Offers a free trial period for analysis, provides detailed insights on placements and keywords, and supports affiliate fraud protection.	Full effectiveness requires installing a tracking tag across all website pages, which might be a technical hurdle for some users.
ClickGUARD	A service designed to monitor, detect, and eliminate fake traffic from PPC campaigns. It gives users granular control to define custom rules for blocking specific traffic patterns and behaviors.	Highly customizable rules, real-time monitoring, and detailed reporting on click fraud patterns.	Platform support may be more limited compared to broader solutions, focusing primarily on PPC campaigns.

📊 KPI & Metrics

Tracking Key Performance Indicators (KPIs) is crucial to measure the effectiveness of fraud prevention based on attribution windows. It's important to monitor not just the volume of fraud detected, but also how its prevention impacts core business outcomes like ad spend efficiency and customer acquisition costs.

Metric Name	Description	Business Relevance
Fraudulent Install Rate	The percentage of total attributed installs that are identified as fraudulent by the system.	Directly measures the scale of the fraud problem and the effectiveness of detection rules.
Click-to-Install Time (CTIT) Distribution	A statistical analysis of the time between clicks and installs for a given traffic source.	Helps identify low-quality sources engaging in click spamming or injection, which skew performance data.
Customer Acquisition Cost (CAC)	The total cost of acquiring a new customer, including ad spend.	Effective fraud prevention lowers CAC by eliminating wasted ad spend on fake users.
Return on Ad Spend (ROAS)	Measures the gross revenue generated for every dollar spent on advertising.	Blocking ad fraud improves ROAS by ensuring the budget is spent on genuine users who can convert.
False Positive Rate	The percentage of legitimate conversions that are incorrectly flagged as fraudulent.	A low rate is critical to ensure that valuable traffic sources are not blocked by overly aggressive rules.

These metrics are typically monitored through real-time dashboards provided by ad fraud detection services or mobile measurement partners. Automated alerts are often configured to notify advertisers of sudden spikes in fraudulent activity or significant deviations in CTIT patterns. This feedback loop allows for the continuous optimization of fraud filters and attribution rules to adapt to new threats while protecting campaign performance.

🆚 Comparison with Other Detection Methods

Real-time vs. Post-Attribution Analysis

Attribution window analysis, particularly CTIT, often happens post-attribution, after an install has occurred and is being considered for attribution. This differs from real-time blocking methods like IP blacklisting or signature-based detection, which aim to prevent the click from ever reaching the advertiser. While real-time methods are faster, attribution window analysis is highly effective at catching sophisticated fraud like click injection that can only be identified by correlating the click and install events.

Behavioral Analytics vs. Timing Heuristics

Behavioral analytics focuses on post-install engagement to identify fraud. It looks for a lack of meaningful user activity after an install, which indicates a fake user. Attribution window analysis, by contrast, uses timing heuristics (the CTIT) as its primary signal. The two are complementary; attribution window analysis can quickly flag suspicious installs based on timing, while behavioral analytics can confirm the fraud by observing a lack of subsequent engagement.

Scalability and Accuracy

Attribution window analysis is highly scalable as it relies on simple time-based calculations. However, its accuracy can be limited. For example, a legitimate user might install an app very quickly, leading to a potential false positive. In contrast, deep learning-based behavioral models may offer higher accuracy but require significantly more data and computational resources. Therefore, many fraud detection systems use attribution window analysis as an efficient first-pass filter before applying more complex methods.

⚠️ Limitations & Drawbacks

While analyzing attribution windows is a powerful technique for fraud detection, it has certain limitations. Its effectiveness can be constrained by the type of fraud, and overly strict rules can inadvertently harm campaign measurement by penalizing legitimate user behavior.

• False Positives – Strict rules on click-to-install times can incorrectly flag legitimate users who install an app very quickly, potentially blocking valid conversions.
• Limited Scope – It is most effective against specific fraud types like click injection and click spamming but less so against sophisticated bots that can mimic human timing.
• Inability to Stop Pre-Bid Fraud – This method primarily analyzes events post-click, meaning it doesn't prevent bots from clicking on ads in the first place; it only stops them from getting attribution credit.
• Dependence on Conversion Events – The technique requires a conversion (like an install) to happen before analysis can be performed, making it a reactive rather than a proactive measure.
• Vulnerability to Sophisticated Spoofing – Advanced fraudsters can program bots to wait for a randomized, "natural" amount of time between a fake click and a fake install, thereby bypassing simple CTIT checks.
• Attribution Window Ambiguity – There is no universally perfect window length; a window that's too long may credit fraudulent events, while one that's too short may miss legitimate, delayed conversions.

In scenarios involving complex, multi-touch user journeys or highly sophisticated bot attacks, a hybrid approach combining attribution analysis with real-time behavioral monitoring is often more suitable.

❓ Frequently Asked Questions

How does a shorter attribution window help prevent fraud?

A shorter attribution window, such as 24 hours instead of 30 days, reduces the opportunity for fraud like click spamming. Fraudsters who generate massive volumes of random clicks have a smaller timeframe to get lucky and have one of their fake clicks credited for an organic install.

Can attribution window analysis stop all types of ad fraud?

No, it is most effective against specific types of attribution fraud like click injection and click spamming. It is less effective against other methods like sophisticated bots that mimic human behavior or install farms using real devices, which may require additional detection layers like behavioral analysis.

What is the difference between an attribution window and a lookback window?

The terms are often used interchangeably and refer to the same concept: the period of time in which a conversion can be credited to a specific ad interaction. Both define the timeframe for linking a cause (the click or view) to an effect (the conversion).

Does this analysis work for both click-through and view-through conversions?

Yes, but the logic and window lengths differ. Click-through attribution uses a longer window (e.g., 7 days) as a click shows clear intent. View-through attribution uses a much shorter window (e.g., 24 hours) because the link between seeing an ad and converting is weaker and more susceptible to fraud.

Can setting a very strict attribution window hurt my campaign?

Yes, it can. If the window is too short, you may fail to attribute legitimate conversions from users who take longer to decide, leading you to undervalue certain channels. This can result in misleading performance data, causing you to make poor budget allocation decisions.

🧾 Summary

An attribution window is the defined time period after an ad engagement during which a conversion can be credited to it. In fraud prevention, this concept is vital for identifying malicious activity by analyzing the click-to-install time (CTIT). Unnaturally short or randomly distributed CTITs are key indicators of fraud, allowing advertisers to reject fake attributions and protect their budgets.

🔗 Related Articles

• What is an attribution window? - Adjust – https://www.adjust.com/glossary/attribution-window/
• What is click to install time (CTIT)? - Adjust – https://www.adjust.com/glossary/click-to-install-time-ctit/
• What is click to install time (CTIT)? | AppsFlyer mobile glossary – https://www.appsflyer.com/glossary/ctit-click-to-install-time/
• Detecting Fraud Based On Click To Install Time - TrafficGuard – https://www.trafficguard.ai/blog/detecting-fraud-based-on-click-to-install-time
• How Ad Fraud Impacts Marketing KPIs - TrafficGuard – https://www.trafficguard.ai/blog/how-ad-fraud-impacts-marketing-kpis
• What is an attribution window? | AppsFlyer mobile glossary – https://www.appsflyer.com/glossary/attribution-window/
• Learn About the SKAdNetwork Attribution Windows - Kochava – https://www.kochava.com/learn-about-the-skadnetwork-attribution-windows/