Lifetime Value (LTV)

How Lifetime ValueLTV Works

Incoming Traffic (Clicks/Impressions)
           │
           ▼
+----------------------+
│ Data Collection      │
│ (IP, UA, Timestamps) │
+----------------------+
           │
           ▼
+----------------------+
│ LTV Model Analysis   │
│ (Behavioral &       │
│  Predictive Logic)   │
+----------------------+
           │
           ├─→ [High LTV] → Legitimate User → Allow Access
           │
           └─→ [Low/Zero LTV] → Suspicious/Bot → Block/Flag

🧠 Core Detection Logic

Example 1: Behavioral Anomaly Detection

This logic identifies users whose behavior patterns deviate significantly from those of genuine, high-value customers. It is applied post-click to analyze session data and flag non-human or unengaged traffic that is unlikely to have any lifetime value.

FUNCTION analyze_session(session_data):
  IF session_data.time_on_page < 2 seconds AND
     session_data.click_count > 5 AND
     session_data.conversion_events == 0 THEN
    
    SET user.predicted_ltv = 0
    RETURN "BLOCK"
    
  ELSE:
    RETURN "ALLOW"

Example 2: Predictive LTV Scoring

This logic uses historical data to predict the future value of a new user based on their initial characteristics. It’s used at the point of acquisition to decide whether to invest in a user from a particular channel or campaign.

FUNCTION predict_ltv(user_attributes):
  // Historical data shows users from 'organic_search' have high LTV
  // and users from 'suspicious_affiliate_network' have zero LTV.
  
  historical_ltv = get_ltv_for_source(user_attributes.source)
  
  IF historical_ltv < threshold.minimum_value THEN
    FLAG user AS "low_quality_acquisition"
    RETURN 0
    
  ELSE:
    RETURN historical_ltv

Example 3: IP Reputation and LTV Correlation

This logic combines traditional IP reputation (e.g., known data center or proxy IPs) with LTV metrics. It assumes that traffic from sources consistently associated with zero-LTV users is fraudulent, even if the IP is not on a standard blacklist.

FUNCTION check_ip_ltv(ip_address):
  // Query historical data for the average LTV of users from this IP
  avg_ltv_for_ip = query_historical_ltv(ip_address)
  
  IF avg_ltv_for_ip == 0 AND 
     get_total_users_from_ip(ip_address) > 10 THEN
    
    ADD ip_address TO "zero_ltv_blocklist"
    RETURN "FRAUDULENT"
    
  ELSE:
    RETURN "LEGITIMATE"

📈 Practical Use Cases for Businesses

• Campaign Shielding – Businesses use LTV models to automatically filter out low-quality traffic sources that deliver clicks with no long-term value. This protects campaign budgets from being wasted on fraudulent publishers or botnets that generate worthless interactions.
• ROAS Optimization – By focusing ad spend on channels that historically deliver high-LTV users, companies improve their Return on Ad Spend. LTV analysis helps identify which campaigns attract loyal customers versus those that attract only single-click, no-value users.
• Clean Analytics – Fraudulent traffic skews key business metrics like conversion rates and user engagement. By blocking zero-LTV traffic, businesses ensure their analytics platforms reflect genuine user behavior, leading to more accurate data-driven decisions.
• User Acquisition Filtering – LTV predictions allow businesses to be more selective in their user acquisition efforts. They can choose to pay more for traffic from sources known to produce high-LTV users and block or pay less for sources that do not.

Example 1: Dynamic Source Blocking Rule

This pseudocode automatically blocks an ad traffic source if the average predicted LTV of its users falls below a set monetary threshold after a certain number of clicks.

FUNCTION evaluate_traffic_source(source_id):
  source_stats = get_stats_for(source_id)
  
  IF source_stats.total_clicks > 500 AND 
     source_stats.average_predicted_ltv < $0.05 THEN
    
    block_source(source_id)
    log_action("Blocked source " + source_id + " due to zero LTV traffic.")
  
  END IF

Example 2: High-Value User Segmentation

This logic identifies users with high predicted LTV and places them into a "premium" audience segment for retargeting, while excluding low-LTV users to save budget.

FUNCTION segment_user(user_id):
  user_ltv = predict_user_ltv(user_id)
  
  IF user_ltv > 100 THEN
    add_to_audience(user_id, "premium_retargeting")
  
  ELSE IF user_ltv < 1 THEN
    add_to_audience(user_id, "exclusion_list")
  
  END IF

🐍 Python Code Examples

This Python function simulates checking if a click's frequency from a single IP address is abnormally high, a common indicator of bot activity which corresponds to zero lifetime value.

CLICK_HISTORY = {}
TIME_WINDOW_SECONDS = 60
MAX_CLICKS_IN_WINDOW = 5

def is_abnormal_frequency(ip_address):
    import time
    current_time = time.time()
    
    if ip_address not in CLICK_HISTORY:
        CLICK_HISTORY[ip_address] = []
    
    # Remove clicks outside the time window
    CLICK_HISTORY[ip_address] = [t for t in CLICK_HISTORY[ip_address] if current_time - t < TIME_WINDOW_SECONDS]
    
    # Add current click
    CLICK_HISTORY[ip_address].append(current_time)
    
    # Check if click count exceeds the maximum
    if len(CLICK_HISTORY[ip_address]) > MAX_CLICKS_IN_WINDOW:
        return True # Abnormal frequency detected
        
    return False

# Example usage:
# print(is_abnormal_frequency("192.168.1.100"))

This code snippet scores traffic based on whether the user-agent string belongs to a known bot or a non-standard browser, which are often associated with fraudulent, zero-LTV traffic.

KNOWN_BOT_UAS = ["Googlebot", "Bingbot", "MyCustomScraper/1.0"]
SUSPICIOUS_UAS = ["HeadlessChrome", "PhantomJS"]

def score_traffic_by_ua(user_agent_string):
    if any(bot_ua in user_agent_string for bot_ua in KNOWN_BOT_UAS):
        return 0  # Known Bot -> Zero LTV

    if any(suspicious_ua in user_agent_string for suspicious_ua in SUSPICIOUS_UAS):
        return 20  # Suspicious -> Low LTV
    
    return 100 # Assumed Legitimate -> High LTV

# Example usage:
# score = score_traffic_by_ua("Mozilla/5.0 HeadlessChrome")
# print(f"Traffic Score: {score}")

Types of Lifetime ValueLTV

• Predictive LTV – This is the most common type in fraud detection. It uses machine learning models and historical data to forecast the total revenue a new user will generate. It's crucial for proactively blocking fraudulent traffic from sources that have a history of delivering zero-value users.
• Historical LTV – This type calculates the actual revenue a user has generated to date by summing up all their past purchases or interactions. While not predictive, it is used to build the datasets needed to train predictive LTV models and validate their accuracy.
• Segment-Based LTV – This approach calculates the average LTV for specific user segments (e.g., by acquisition channel, geography, or initial action). In fraud prevention, it helps identify and cut spending on entire segments that consistently produce low-LTV or fraudulent users.
• Behavioral LTV – This variation focuses on non-monetary actions that correlate with long-term value, such as frequency of visits, session duration, and feature adoption. It helps detect sophisticated bots that mimic initial sign-ups but show no deep engagement, indicating they have no real LTV.