Click To Install Time (CTIT)

How Click To Install Time CTIT Works

User Journey & Detection Pipeline

[Ad Click] → [Redirect to App Store] → [App Download] → [First App Open]
     |                                                          |
     └─────────── Capture Timestamp 1 (T1) ───────────┘
                                                            |
                                                            ▼
                                   +----------------------------------+
                                   |   Fraud Detection System         |
                                   +----------------------------------+
                                   | 1. Capture Timestamp 2 (T2)      |
                                   | 2. Calculate CTIT = T2 - T1      |
                                   | 3. Analyze CTIT Distribution     |
                                   | 4. Flag Anomalies (Too Short/Long) |
                                   └─────────+------------------------+
                                             |
                                             ▼
                                     [Valid Install] or [Fraudulent Install]

Click To Install Time (CTIT) is a fundamental metric in mobile ad fraud detection that works by measuring the time elapsed between two key events: the ad click and the first launch of the installed application. This simple duration provides powerful insights into the legitimacy of an install. Traffic security systems leverage CTIT by establishing baseline patterns for normal user behavior and flagging deviations that suggest fraudulent activity.

Timestamp Capturing

The process begins the moment a user clicks on an ad. The ad network or attribution provider records a precise timestamp for this event (T1). Subsequently, when the user downloads, installs, and finally opens the app for the first time, the measurement SDK embedded within the app records a second timestamp (T2). These two timestamps are the raw data points required for CTIT analysis.

Calculation and Distribution Analysis

The core of the mechanism is the calculation: CTIT = First App Open Timestamp – Ad Click Timestamp. A single CTIT value is not enough; fraud detection systems analyze the distribution of these values over thousands of installs for a given campaign. Legitimate traffic typically forms a predictable curve (often a log-normal distribution), where most installs happen within a reasonable timeframe. Fraudulent traffic, however, disrupts this pattern.

Anomaly Detection

Fraud is flagged when CTIT values fall into anomalous ranges. An extremely short CTIT (e.g., under 10 seconds) is a strong indicator of click injection, where a fraudulent app triggers a click just moments before an install completes to steal attribution. Conversely, an abnormally long or random distribution of CTITs can indicate click spamming, where fake clicks are generated continuously, hoping to claim credit for organic installs.

Diagram Breakdown

The ASCII diagram illustrates this flow. The “User Journey” shows the natural progression from an ad click to the first app open. The “Detection Pipeline” runs in parallel. It captures the start (T1) and end (T2) timestamps of this journey. Inside the “Fraud Detection System,” the CTIT is calculated and compared against expected patterns. The outcome of this analysis determines whether the install is classified as valid or fraudulent, protecting ad budgets from being spent on fake users.

🧠 Core Detection Logic

Example 1: Timestamp Anomaly Detection

This logic flags installs that occur too quickly or too slowly after a click, which are strong indicators of specific fraud types. Very short durations often point to click injection, while very long durations can suggest click spamming. This is a first-line defense in traffic filtering.

// Define time thresholds in seconds
MIN_THRESHOLD = 10;
MAX_THRESHOLD = 86400; // 24 hours

FUNCTION check_ctit_anomaly(click_timestamp, install_timestamp):
  ctit = install_timestamp - click_timestamp;

  IF ctit < MIN_THRESHOLD:
    RETURN "FLAGGED: Potential Click Injection";
  ELSE IF ctit > MAX_THRESHOLD:
    RETURN "FLAGGED: Potential Click Spamming";
  ELSE:
    RETURN "VALID";
  END IF
END FUNCTION

Example 2: CTIT Distribution Analysis for a Publisher

This logic moves beyond single installs to analyze the overall pattern of a traffic source (e.g., a publisher). It calculates the percentage of installs that have an abnormally short CTIT. If this percentage exceeds a certain tolerance, the entire publisher might be flagged for review or automatically blocked.

// Define publisher-level thresholds
PUBLISHER_ID = "pub12345";
SUSPICIOUS_CTIT_LIMIT = 15; // seconds
TOLERANCE_PERCENTAGE = 5.0; // % of installs

FUNCTION analyze_publisher_ctit(installs_data):
  suspicious_installs = 0;
  total_installs = 0;

  FOR each install IN installs_data:
    IF install.publisher == PUBLISHER_ID:
      total_installs += 1;
      ctit = install.timestamp - install.click_timestamp;
      IF ctit < SUSPICIOUS_CTIT_LIMIT:
        suspicious_installs += 1;
      END IF
    END IF
  END FOR

  suspicious_percentage = (suspicious_installs / total_installs) * 100;

  IF suspicious_percentage > TOLERANCE_PERCENTAGE:
    RETURN "FLAGGED: Publisher has high rate of suspicious installs";
  ELSE:
    RETURN "VALID";
  END IF
END FUNCTION

Example 3: IP and CTIT Velocity Check

This logic identifies situations where multiple installs from the same IP address have nearly identical and often very fast CTITs. This pattern is highly unnatural for human behavior and strongly suggests a bot or a device farm is being used to generate fraudulent installs.

// Store recent installs with their IP and CTIT
install_records = {}; // Format: {ip: [{timestamp, ctit}, ...]}

FUNCTION check_ip_velocity(new_install):
  ip = new_install.ip_address;
  ctit = new_install.timestamp - new_install.click_timestamp;

  IF ip in install_records:
    FOR each past_install IN install_records[ip]:
      // Check if another install happened within 60s with a similar CTIT (+/- 5s)
      time_difference = new_install.timestamp - past_install.timestamp;
      ctit_difference = abs(ctit - past_install.ctit);

      IF time_difference < 60 AND ctit_difference < 5:
        RETURN "FLAGGED: High velocity of similar CTITs from same IP";
      END IF
    END FOR
  END IF

  // Add current install to records for future checks
  add_record(ip, new_install.timestamp, ctit);
  RETURN "VALID";
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Budget Protection: By filtering out installs with fraudulent CTIT patterns, businesses prevent ad spend from being wasted on fake users, directly protecting their marketing budget.
• Improved Return on Ad Spend (ROAS): Eliminating fraud ensures that attribution data is clean. This allows marketers to allocate their budget to high-performing, legitimate channels, thereby improving overall campaign ROI.
• Accurate Performance Analytics: Clean data leads to trustworthy metrics. By removing fraudulent installs, businesses can get a true picture of their Key Performance Indicators (KPIs), leading to better strategic decisions.
• Enhanced User Journey Optimization: Analyzing CTIT distributions can reveal friction in the user onboarding process. A consistently long (but legitimate) CTIT might indicate slow downloads or a complex registration, prompting optimization.

Example 1: Publisher Quality Scoring Rule

A business rule that automatically scores publishers based on the health of their CTIT distribution. Publishers who consistently provide traffic with CTITs that fall within a "healthy" range receive a higher quality score, while those with many anomalies are scored lower and may be automatically paused.

// Pseudocode for publisher scoring
FUNCTION calculate_publisher_score(publisher_id, install_data):
  installs = get_installs_for_publisher(publisher_id, install_data);
  total_installs = length(installs);
  healthy_installs = 0;

  FOR each install IN installs:
    ctit = install.open_time - install.click_time;
    IF ctit >= 15 AND ctit <= 3600: // 15 seconds to 1 hour
      healthy_installs += 1;
    END IF
  END FOR

  health_ratio = healthy_installs / total_installs;
  
  IF health_ratio < 0.7:
    RETURN "POOR_QUALITY";
  ELSE IF health_ratio < 0.9:
    RETURN "AVERAGE_QUALITY";
  ELSE:
    RETURN "HIGH_QUALITY";
  END IF
END FUNCTION

Example 2: Real-time Rejection of Injected Clicks

This logic is used in real-time to reject an install attribution before it's ever recorded if the CTIT is impossibly short. This is a critical rule for combating click injection fraud on Android devices.

// Pseudocode for real-time click injection rejection
FUNCTION process_install_attribution(click, install_open):
  MIN_LEGITIMATE_CTIT = 10; // 10 seconds

  ctit = install_open.timestamp - click.timestamp;

  IF ctit < MIN_LEGITIMATE_CTIT:
    // Reject the attribution and do not credit the click source
    REJECT_ATTRIBUTION(click.source, "Click Injection Detected");
    RETURN "REJECTED";
  ELSE:
    // Grant attribution to the click source
    GRANT_ATTRIBUTION(click.source);
    RETURN "SUCCESS";
  END IF
END FUNCTION

🐍 Python Code Examples

This Python function simulates the basic logic for classifying an install as valid or fraudulent based on predefined CTIT thresholds. It is a foundational check in any ad fraud detection system.

from datetime import datetime, timedelta

def check_ctit(click_time_str, install_time_str):
    """
    Analyzes the time between a click and an install to flag potential fraud.
    """
    click_time = datetime.fromisoformat(click_time_str)
    install_time = datetime.fromisoformat(install_time_str)
    
    ctit_delta = install_time - click_time
    
    # Flag installs happening in under 10 seconds as likely click injection
    if ctit_delta < timedelta(seconds=10):
        return f"Fraudulent: CTIT of {ctit_delta.seconds}s is too short (Potential Click Injection)"
        
    # Flag installs taking longer than a day as potential click spam
    if ctit_delta > timedelta(days=1):
        return f"Suspicious: CTIT of {ctit_delta.days} days is too long (Potential Click Spam)"
        
    return "Valid: CTIT is within normal range."

# Example Usage
click = "2025-07-15T10:00:00"
install_too_fast = "2025-07-15T10:00:05"
install_normal = "2025-07-15T10:05:30"
install_too_slow = "2025-07-17T11:00:00"

print(f"Install 1: {check_ctit(click, install_too_fast)}")
print(f"Install 2: {check_ctit(click, install_normal)}")
print(f"Install 3: {check_ctit(click, install_too_slow)}")

This example demonstrates analyzing a list of installs from a single source to detect abnormal patterns. It calculates the percentage of suspiciously fast installs, which can indicate a fraudulent publisher or sub-source that should be blocked.

def analyze_traffic_source(install_events, source_id, threshold_seconds=15, tolerance_percent=5.0):
    """
    Analyzes a list of install events from a source to check for widespread fraud.
    """
    source_installs = [event for event in install_events if event['source_id'] == source_id]
    if not source_installs:
        return f"No installs found for source {source_id}."

    suspicious_count = 0
    for event in source_installs:
        click_time = datetime.fromisoformat(event['click_time'])
        install_time = datetime.fromisoformat(event['install_time'])
        
        if (install_time - click_time) < timedelta(seconds=threshold_seconds):
            suspicious_count += 1
            
    suspicious_percentage = (suspicious_count / len(source_installs)) * 100
    
    if suspicious_percentage > tolerance_percent:
        return f"Block Source {source_id}: {suspicious_percentage:.2f}% of installs are suspicious."
    else:
        return f"Monitor Source {source_id}: {suspicious_percentage:.2f}% of installs are suspicious."

# Example Data
installs = [
    {'source_id': 'publisher_A', 'click_time': '2025-07-15T10:01:00', 'install_time': '2025-07-15T10:05:00'},
    {'source_id': 'publisher_B', 'click_time': '2025-07-15T10:02:00', 'install_time': '2025-07-15T10:02:04'},
    {'source_id': 'publisher_B', 'click_time': '2025-07-15T10:03:00', 'install_time': '2025-07-15T10:03:06'},
    {'source_id': 'publisher_B', 'click_time': '2025-07-15T10:04:00', 'install_time': '2025-07-15T10:14:00'},
]

print(analyze_traffic_source(installs, 'publisher_A'))
print(analyze_traffic_source(installs, 'publisher_B'))

Types of Click To Install Time CTIT

• Short CTIT: This pattern involves installs occurring within a few seconds of a click. It is a primary indicator of click injection fraud, where malware on a device sends a fraudulent click just before an install completes to steal credit for the attribution.
• Long CTIT: This pattern involves installs happening hours or even days after the attributed click. It is often a sign of click spamming (or click flooding), where fraudsters generate a high volume of random clicks, hoping one will be credited for a later organic install.
• Normal CTIT: Representing legitimate user behavior, this pattern shows a moderate and logical time between click and install. Analysis often reveals a bell-curve or log-normal distribution, which security systems use as a benchmark to identify anomalies.
• Flat or Dispersed CTIT: This pattern lacks a clear peak and shows installs spread out randomly over a long period. This is characteristic of click spamming, as the fraudulent clicks have no real correlation with when users actually decide to install an app.

🛡️ Common Detection Techniques

• Time-to-Install (TTI/CTIT) Analysis: This core technique measures the duration between an ad click and the first app open. Abnormally short or long times are flagged, effectively detecting fraud like click injection and click spamming.
• Distribution Modeling: Instead of fixed thresholds, this technique analyzes the statistical distribution of CTITs for a campaign. It identifies fraudulent sources by spotting patterns that deviate significantly from the natural curve of legitimate user behavior.
• IP-Based Correlation: This method checks for anomalies tied to IP addresses, such as an unusually high number of installs from a single IP, or multiple installs sharing the exact same short CTIT, which indicates bot activity.
• New Device Rate (NDR) Monitoring: While not directly CTIT, this is often used alongside it. An unusually high rate of "new" devices from one source, combined with suspicious CTIT patterns, can indicate device farm activity where device IDs are repeatedly reset.
• Click to Click Time (CTCT) Analysis: This technique measures the time between consecutive clicks from the same device. Very short intervals between clicks for different apps can indicate a device is being used for click spamming, which helps contextualize anomalous CTIT data.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
Adjust	A mobile measurement partner with a comprehensive Fraud Prevention Suite. It uses CTIT distribution modeling and real-time click injection filtering to block and report fraudulent traffic, protecting ad spend and ensuring data accuracy.	Real-time filtering, automated distribution analysis, detailed rejection reasons, customizable alerts.	Can be complex to configure custom rules. Cost may be a factor for smaller advertisers.
AppsFlyer	Offers a fraud protection solution called Protect360 that uses CTIT analysis as a core component. It identifies install hijacking (short CTIT) and click flooding (long CTIT) by analyzing distribution patterns and other signals.	Layered protection, post-attribution fraud detection, large-scale data for modeling, identifies new fraud patterns.	Full feature set is part of a premium suite. The sheer volume of data and options can be overwhelming initially.
TrafficGuard	A dedicated ad fraud prevention platform that uses CTIT analysis as a key indicator. It identifies high volumes of traffic outside the normal time window between click and install to detect and block invalid traffic in real-time.	Specializes solely in fraud prevention, offers real-time blocking, covers multiple campaign types (PPC, app installs).	Requires integration alongside an existing analytics or MMP platform. Focus is purely on fraud, not holistic campaign analytics.
MyTracker	An analytics and attribution platform with built-in fraud detection capabilities. It uses CTIT, View to Install Time (VTIT), and Click to Click Time (CTCT) metrics to detect anomalies and identify different types of ad fraud.	Combines multiple time-based metrics for more robust detection, offers a clear view of the user journey, provides fraud reports.	May not have the same scale of global data as larger MMPs, potentially affecting the breadth of its fraud detection models.

📊 KPI & Metrics

Tracking the right KPIs is crucial to measure both the technical effectiveness of CTIT analysis and its impact on business goals. Monitoring these metrics helps justify investment in fraud prevention and ensures that detection rules are not inadvertently harming legitimate user acquisition efforts.

Metric Name	Description	Business Relevance
Fraudulent Install Rate	The percentage of total installs flagged as fraudulent based on CTIT and other rules.	Directly measures the volume of fraud being caught and indicates the cleanliness of traffic sources.
False Positive Rate	The percentage of legitimate installs incorrectly flagged as fraudulent by CTIT rules.	Crucial for ensuring that strict anti-fraud rules are not blocking real users and potential revenue.
Cost Per Install (CPI) Reduction	The decrease in effective CPI after fraudulent installs are filtered and budgets are reallocated.	Demonstrates the direct financial savings and improved budget efficiency from using CTIT analysis.
Clean Traffic Ratio	The ratio of valid installs to total attributed installs from a specific channel or publisher.	Helps in evaluating the quality of traffic sources and making data-driven partnership decisions.

These metrics are typically monitored through real-time dashboards provided by mobile measurement partners or dedicated fraud detection platforms. Automated alerts can be configured to notify marketers of sudden spikes in fraudulent activity or significant changes in these KPIs. This feedback loop is essential for continuously optimizing fraud filters and adapting to new threats without disrupting campaign performance.

🆚 Comparison with Other Detection Methods

CTIT Analysis vs. Signature-Based Filtering

Signature-based filtering relies on known patterns of fraud, such as blacklisted IP addresses, known fraudulent device IDs, or specific user-agent strings. While it is very fast and effective against known threats, it is reactive and cannot detect new or unknown fraud patterns. CTIT analysis, by contrast, is a behavioral method. It detects anomalies in the *timing* of user actions, allowing it to identify new forms of click injection or click spamming without a pre-existing signature. However, it can be more resource-intensive than simple blacklist lookups.

CTIT Analysis vs. Deep Behavioral Analytics

Deep behavioral analytics involves a much broader examination of post-install user activity—such as level completions in a game, purchase events, or general app engagement. This method is excellent for detecting sophisticated bots that may have a realistic CTIT but show no real human engagement later on. CTIT analysis is a much faster, pre-attribution check that serves as a crucial first line of defense. It is less complex but also less effective on its own against bots programmed to mimic early funnel behavior perfectly. The most robust fraud solutions use CTIT analysis for immediate filtering and deep behavioral analysis for post-attribution validation.

⚠️ Limitations & Drawbacks

While Click To Install Time (CTIT) is a powerful tool in fraud detection, it is not foolproof. Its effectiveness can be limited by the sophistication of fraud schemes and the specific context of an ad campaign. Relying solely on CTIT can lead to both missed fraud and the incorrect blocking of legitimate users.

• False Positives: Strict CTIT thresholds may incorrectly flag legitimate users with very fast internet connections or devices (short CTIT) or those who get distracted between the click and the first app open (long CTIT).
• Sophisticated Bots: Advanced bots can be programmed to delay actions, mimicking a more realistic CTIT and thereby evading simple threshold-based detection.
• Inability to Verify Intent: CTIT measures the 'what' (the time), not the 'why'. It cannot distinguish between a fraudulent long CTIT from click spamming and a legitimate user who clicked an ad, forgot, and then organically found and opened the app hours later.
• Network and Device Variability: Legitimate CTIT can vary widely based on geographic location, network quality, and device performance, making it difficult to set a single "correct" threshold for all traffic.
• Focus on a Single Metric: CTIT only covers one aspect of the user journey. It is blind to other forms of fraud like SDK spoofing, fake in-app events, or compliance fraud that occur post-install.

Because of these limitations, CTIT is most effective when used as part of a multi-layered fraud detection strategy that includes other signals like IP reputation, device fingerprinting, and post-install behavioral analysis.

❓ Frequently Asked Questions

How does CTIT help detect click injection?

Click injection fraud occurs when a fraudulent app on a user's device detects an app installation and triggers a fake click just before the installation completes. This results in an abnormally short CTIT, often just a few seconds, which is a strong statistical signal that fraud detection systems use to block the attribution.

Can a very short Click To Install Time ever be legitimate?

While technically possible with extremely fast internet and a high-performance device, a CTIT of less than 10 seconds is highly improbable for a real user. The process involves clicking the ad, being redirected to the app store, authentication, downloading, installing, and opening the app. An extremely short time is almost always an indicator of fraud.

Is CTIT analysis effective for iOS and Android?

Yes, but it is particularly critical for Android. While the concept applies to both, the Android operating system's use of "install broadcasts" makes it more vulnerable to click injection, a fraud type that CTIT is exceptionally good at catching. For both platforms, it remains a key metric for detecting click spamming.

Why is analyzing the CTIT distribution more effective than using a fixed rule?

A fixed rule (e.g., 'block all installs under 10 seconds') is a good start, but legitimate CTIT can vary by country, app size, and network. Analyzing the entire distribution pattern allows fraud systems to learn what is 'normal' for a specific campaign and identify sources whose pattern deviates from that norm, making it a more adaptive and accurate detection method.

Is CTIT analysis sufficient to stop all mobile ad fraud?

No, CTIT is a crucial tool but not a complete solution. It is most effective against specific types of fraud like click spamming and click injection. Sophisticated fraudsters can use bots to mimic human CTIT or employ other fraud types like SDK spoofing or generating fake in-app events, which require additional detection methods like behavioral analysis and IP filtering.

🧾 Summary

Click To Install Time (CTIT) is a core metric in digital advertising that measures the time between an ad click and the subsequent first app open. Its primary role in traffic protection is to identify fraudulent activity by detecting abnormal time intervals. Unusually short times indicate click injection, while long or random times suggest click spamming, enabling advertisers to protect budgets and ensure data integrity.

🔗 Related Articles

• What is click to install time (CTIT)? - Adjust – https://www.adjust.com/glossary/click-to-install-time-ctit/
• Click to install time (CTIT) | AppsFlyer mobile glossary – https://www.appsflyer.com/glossary/click-to-install-time-ctit/
• Mobile ad fraud metrics explained: CTIT, VTIT, and CTCT - MyTracker – https://mytracker.com/blog/mobile-ad-fraud-metrics-explained-ctit-vtit-and-ctct/
• Detecting Fraud Based On Click To Install Time - TrafficGuard – https://www.trafficguard.ai/blog/detecting-fraud-based-on-click-to-install-time
• The marketer's field guide to mobile ad fraud - AppsFlyer – https://www.appsflyer.com/resources/reports/marketers-field-guide-mobile-ad-fraud/