Retail media networks

How Retail media networks Works

Ad Request from User → Retail Media Network → +---------------------------+
                                          |   Fraud Detection Layer   |
                                          +---------------------------+
                                                       |
                                                       ├─ Legitimate Traffic → Ad Served to User
                                                       |
                                                       └─ Fraudulent Traffic → Blocked & Logged

A Retail Media Network’s (RMN) defense against ad fraud stems from its unique access to rich, first-party customer data. Unlike ad platforms on the open internet that rely on less reliable third-party signals, RMNs can verify ad interactions against a wealth of internal information about real shoppers. This creates a high-fidelity validation system that is difficult for bots and fraudsters to bypass. The entire process hinges on connecting ad activity with actual shopping behavior to ensure advertisers are paying to reach genuine consumers.

Data Ingestion and First-Party Advantage

At its core, an RMN ingests and processes vast amounts of proprietary data. This includes online and offline purchase histories, loyalty program memberships, on-site search queries, and product browsing behavior. This trusted, closed-loop dataset serves as the ground truth for what constitutes a legitimate customer, forming the foundation of the fraud detection process. Bots and automated scripts simply do not have this kind of history with the retailer.

Real-Time Traffic Analysis

When a user is about to be served an ad on a retailer’s website or app, the RMN initiates a real-time analysis of the ad request. It inspects standard data points like IP address, device type, and user agent, but crucially cross-references the user’s identifier (such as a customer ID or cookie) with its first-party database. A request from a recognized, active shopper is immediately given a high trust score, while an unknown or suspicious identifier is flagged for further scrutiny.

Behavioral Verification and Mitigation

For traffic that isn’t instantly verifiable, the RMN analyzes behavioral signals to distinguish between human interest and bot activity. It assesses whether the user’s on-site behavior is consistent with a typical shopping journey (e.g., browsing multiple products, reasonable time on page) or indicative of fraud (e.g., immediate clicks on high-value ads, no prior browsing). Traffic deemed fraudulent is blocked from seeing the ad, preventing the click or impression from ever occurring and wasting ad spend. This process ensures a cleaner, more effective advertising ecosystem.

ASCII Diagram Breakdown

Ad Request → Retail Media Network

This shows the initial step where a user’s browser or app requests an advertisement from the retailer’s platform as they navigate the site.

Fraud Detection Layer

This central block represents the RMN’s proprietary system where the verification happens. It uses the retailer’s first-party data and behavioral models to analyze the incoming ad request for signs of fraud before an ad is served.

Legitimate Traffic → Ad Served

This path shows a request that has been validated against customer data and behavioral checks. It is confirmed as a real shopper, and the ad is subsequently displayed to them.

Fraudulent Traffic → Blocked & Logged

This path represents a request that has been identified as non-human or invalid. The RMN blocks the ad from being served to this source, and the incident is logged for analysis, protecting the advertiser’s budget.

🧠 Core Detection Logic

Example 1: Cross-Referencing with Purchase History

This logic checks if a user associated with an ad click has a history of making purchases. A consistent record of buying products is a strong signal of a legitimate human shopper, whereas a high volume of ad clicks from users with no purchase history is a significant red flag for bot activity.

FUNCTION verifyClickByPurchase(clickEvent)
  userID = clickEvent.getUserID()
  userPurchaseHistory = queryRetailDB(userID, "purchases")

  IF userPurchaseHistory.hasTransactions() == TRUE
    RETURN "VALID_CLICK"
  ELSE
    // User has never bought anything; apply further scrutiny
    IF isSuspicious(clickEvent.getBehavior())
      RETURN "FRAUDULENT_CLICK"
    END IF
  END IF
  RETURN "UNKNOWN"
END FUNCTION

Example 2: On-Site Behavior Validation

This method validates an ad click by analyzing the user’s broader session activity on the retailer’s site. A click is considered more legitimate if it is preceded by relevant user-initiated actions, like using the search bar, browsing related categories, or adding items to the cart. Clicks without any prior engagement are suspicious.

FUNCTION checkOnsiteEngagement(session)
  adClicks = session.countAdClicks()
  organicActions = session.countOrganicActions() // e.g., search, category view

  // Penalize sessions with ad clicks but no other meaningful interactions
  IF adClicks > 0 AND organicActions == 0
    session.setFraudScore(0.9) // High probability of fraud
    RETURN FALSE
  END IF

  // Reward sessions where browsing precedes ad clicks
  IF session.getTimeToFirstAdClick() > 30 // seconds
    session.setFraudScore(0.1) // Low probability of fraud
    RETURN TRUE
  END IF

  RETURN TRUE
END FUNCTION

Example 3: Loyalty Program Membership Check

This logic prioritizes traffic from users enrolled in the retailer’s loyalty program. Since enrollment requires verifiable personal information, loyalty members are considered high-confidence, pre-vetted customers. This allows the system to fast-track their traffic while focusing resources on analyzing unknown users.

FUNCTION assessTrafficByLoyalty(request)
  userID = request.getUserID()
  isMember = isLoyaltyMember(userID)

  IF isMember == TRUE
    request.setTrafficQuality("PREMIUM_VERIFIED")
    // Minimal fraud checks needed
    RETURN "VERIFIED_USER"
  ELSE
    request.setTrafficQuality("STANDARD_UNVERIFIED")
    // Proceed with full fraud analysis pipeline
    RETURN "UNVERIFIED_USER"
  END IF
END FUNCTION

📈 Practical Use Cases for Businesses

• Campaign Shielding – Protect advertising budgets by using first-party purchase data to ensure ads are served only to verified, human shoppers, not automated bots.
• ROAS Optimization – Improve Return on Ad Spend (ROAS) by filtering out fraudulent traffic, which ensures that performance metrics reflect genuine customer engagement and purchases.
• Clean Analytics – Achieve more accurate campaign reporting and analytics by removing the “noise” of invalid clicks and impressions, leading to better strategic decisions.
• Supply Chain-Informed Advertising – Link ad serving to real-time inventory levels to prevent advertising products that are out of stock, which protects user experience and avoids wasted ad spend.

Example 1: IP Filtering Rule for Data Centers

Retailers can dramatically reduce bot traffic by blocking IP addresses known to belong to data centers and cloud computing networks, as legitimate residential shoppers do not use these services. This rule proactively stops a major source of automated fraud before it can impact campaigns.

FUNCTION handleRequest(request)
  ipAddress = request.getIP()
  
  IF isDataCenterIP(ipAddress)
    // Block traffic originating from known server farms, not consumer ISPs
    blockRequest(request, "Reason: Data Center IP")
    RETURN
  END IF

  serveAd(request)
END FUNCTION

Example 2: Session Scoring Based on Engagement

This logic scores a user’s session quality based on the depth of their interaction. A session with activities like searching, filtering, and viewing multiple pages gets a high score, while a session with only a single page view and an ad click gets a low score and is flagged as suspicious.

FUNCTION scoreSession(session)
  score = 0
  IF session.getPagesViewed() > 2
    score += 10
  END IF
  IF session.usedSearch() == TRUE
    score += 15
  END IF
  IF session.getDuration() < 5 // seconds
    score -= 20
  END IF

  IF score < 5
    flagSessionForReview(session.id)
  END IF
  
  RETURN score
END FUNCTION

🐍 Python Code Examples

This Python function simulates checking for abnormal click frequency. It tracks clicks per user ID within a short time window and flags users who exceed a reasonable threshold, a common indicator of automated bot behavior rather than genuine customer interest.

from collections import defaultdict
import time

CLICK_TIMESTAMPS = defaultdict(list)
TIME_WINDOW = 60  # seconds
MAX_CLICKS_PER_WINDOW = 5

def is_click_fraudulent(user_id):
    """Flags a user if they click too frequently in a given time window."""
    current_time = time.time()
    
    # Remove old timestamps outside the window
    CLICK_TIMESTAMPS[user_id] = [t for t in CLICK_TIMESTAMPS[user_id] if current_time - t < TIME_WINDOW]
    
    # Add the new click timestamp
    CLICK_TIMESTAMPS[user_id].append(current_time)
    
    # Check if the number of clicks exceeds the limit
    if len(CLICK_TIMESTAMPS[user_id]) > MAX_CLICKS_PER_WINDOW:
        print(f"Fraud Warning: User {user_id} exceeded click frequency limits.")
        return True
        
    return False

# Simulation
print(is_click_fraudulent("user-123"))  # False
# ... 5 more rapid clicks from user-123
print(is_click_fraudulent("user-123"))  # True

This code provides a simple filter to identify suspicious user agents. Many bots use generic or non-standard user-agent strings, and this function checks an incoming request against a list of common bot identifiers while allowing known, legitimate browser agents.

def is_suspicious_user_agent(user_agent_string):
    """Identifies requests from known bot-like or outdated user agents."""
    suspicious_signatures = ["bot", "spider", "headlesschrome", "dataprovider"]
    legitimate_browsers = ["Mozilla/", "Chrome/", "Safari/", "Edge/"]
    
    ua_lower = user_agent_string.lower()
    
    for signature in suspicious_signatures:
        if signature in ua_lower:
            return True
            
    # Also flag if it doesn't look like a standard browser
    if not any(browser in user_agent_string for browser in legitimate_browsers):
        return True
        
    return False

# Simulation
ua_bot = "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)"
ua_human = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"

print(f"Bot check: {is_suspicious_user_agent(ua_bot)}")      # True
print(f"Human check: {is_suspicious_user_agent(ua_human)}")  # False

Types of Retail media networks

• On-Site RMNs

  These networks serve ads exclusively on the retailer's own digital properties, such as its website and app. They offer the strongest fraud protection because they have direct, real-time access to a user's complete on-site behavior and purchase history for verification.

• Off-Site Extension RMNs

  These networks use the retailer's first-party data to target ads to the same shoppers on other platforms across the open internet. Fraud detection is more challenging as they lose direct visibility of the user's behavior, making them more reliant on data matching and the security of their media partners.

• In-Store Digital RMNs

  This type involves digital screens and interactive kiosks within physical retail locations. Fraud here is less about bots and more about ensuring ads are actually displayed correctly and that impression counts are valid, which may be verified using sensors or computer vision analytics.

• Hybrid RMNs

  These networks offer a combination of on-site, off-site, and in-store advertising opportunities. From a fraud perspective, they must manage a complex mix of risks, applying robust on-site verification while also relying on partner controls and audits for their off-site and in-store inventory.

🛡️ Common Detection Techniques

• First-Party Data Matching

  This technique involves cross-referencing an ad interaction with the retailer's customer database. If the user associated with a click is a known, active shopper, the interaction is considered legitimate, providing a powerful defense against non-customer bots.

• Purchase History Analysis

  This method validates traffic by checking if the user has a history of making purchases. A user who buys products is almost certainly a real human, making this a high-confidence signal to differentiate shoppers from fraudulent actors who only interact with ads.

• On-Site Behavioral Analysis

  This involves monitoring a user's navigation patterns, search queries, and session duration to see if they align with genuine shopping interest. Automated bots often exhibit unnatural, linear behaviors that can be flagged when compared to the more varied patterns of human shoppers.

• Closed-Loop Attribution

  This technique connects ad exposure directly to a subsequent purchase, both online and in-store. While primarily a measurement tool, it also serves as a powerful fraud filter by validating that ad spend leads to real sales, thereby confirming the quality of the traffic.

• IP and Geo-Filtering

  This method involves blocking traffic from IP addresses associated with data centers, VPNs, or geographic locations known for high levels of fraudulent activity. It is a foundational technique for proactively eliminating common sources of automated bot traffic.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
ShopperVerity Platform	Focuses on real-time validation by cross-referencing user activity against purchase history and loyalty program data to confirm the legitimacy of traffic before serving an ad.	Extremely high accuracy for returning customers; leverages the RMN's strongest proprietary data assets.	Less effective at validating new shoppers with no prior history; can be resource-intensive.
CartGuard Analytics	A behavioral analytics tool that models the entire shopping session, flagging users whose behavior deviates from typical patterns (e.g., clicking ads without browsing).	Good at catching sophisticated bots that mimic human clicks but not human browsing; can identify fraud from new users.	May generate false positives by flagging atypical but legitimate human behavior.
Retail-ID Shield	An identity-based solution that integrates with the retailer's customer login and loyalty systems to prioritize and protect traffic from known, authenticated users.	Provides a strong, positive signal for legitimate traffic; integrates well with personalization efforts.	Only protects traffic from logged-in users, leaving guest traffic vulnerable.
CleanShelf API	An API that allows third-party advertisers (e.g., CPG brands) to receive transparent reports on the fraud filtering applied to their specific campaigns running on the RMN.	Increases advertiser trust and transparency; allows for independent verification of traffic quality.	Relies on the RMN for data access; provides post-campaign analysis rather than pre-bid prevention.

📊 KPI & Metrics

To effectively manage fraud protection within a retail media network, it's crucial to track metrics that measure both the accuracy of the detection technology and the tangible business outcomes. Monitoring these key performance indicators (KPIs) helps ensure that fraud prevention efforts are not only blocking bad traffic but also protecting revenue and the experience of legitimate customers.

Metric Name	Description	Business Relevance
Validated Click Rate	The percentage of total clicks confirmed as legitimate based on first-party data and behavioral analysis.	Measures the core effectiveness of the fraud filtering system and the overall quality of traffic.
Invalid Traffic (IVT) Rate	The percentage of ad traffic identified and blocked as fraudulent or non-human.	Directly shows the volume of fraud being prevented, demonstrating the tool's protective value.
False Positive Rate	The percentage of legitimate human users incorrectly flagged as fraudulent.	A critical balancing metric to ensure fraud filters are not blocking real customers and harming sales.
ROAS on Verified Traffic	Return on Ad Spend calculated using only the budget spent on verified, human traffic.	Provides a true measure of campaign performance by removing the distorting effect of fraudulent clicks.

These metrics are typically monitored through real-time dashboards that visualize traffic patterns and alert teams to anomalies, such as a sudden spike in invalid traffic from a specific source. Feedback from this monitoring is essential for continuously tuning the fraud detection algorithms, updating blocklists, and adapting to new threats, thereby maintaining the integrity of the advertising environment.

🆚 Comparison with Other Detection Methods

Detection Accuracy

Retail media networks generally achieve higher detection accuracy than generic fraud solutions. This is because RMNs leverage high-fidelity, first-party data like verified purchase history and loyalty status—signals that are nearly impossible for bots to fake. In contrast, traditional methods rely on more general signals like IP reputation and device fingerprinting, which can be spoofed, leading to more false positives and negatives.

Data Richness and Context

The key differentiator for RMNs is the rich, contextual data they possess. They can analyze not just the click itself, but the entire shopping journey associated with a user. A standard behavioral analytics tool might see a click as legitimate based on mouse movement, but an RMN can see that the user has never made a purchase and is only clicking on ads, providing a much clearer context for fraud.

Effectiveness Against Sophisticated Bots

RMNs are more effective against sophisticated bots designed to mimic human behavior. While these bots might simulate realistic session durations or click patterns, they cannot fabricate a legitimate, long-term purchase history or an authenticated customer account. This "commercial footprint" is the RMN's unique advantage, creating a verification layer that other systems lack.

⚠️ Limitations & Drawbacks

While powerful, the fraud detection capabilities of retail media networks are not without weaknesses. Their effectiveness is highly dependent on the quality and scope of their first-party data, creating blind spots where this data is unavailable or less relevant, potentially reducing their efficiency or allowing certain types of fraud to go undetected.

• New Customer Blind Spot – The system is less effective at validating new shoppers who have no purchase or browsing history, as they may be incorrectly flagged as suspicious or lack the data for a confident verification.
• Limited Off-Site Visibility – When extending campaigns to the open web, RMNs lose direct sight of user behavior and must rely on partners, increasing exposure to ad fraud and MFA sites.
• Data Privacy Constraints – Evolving privacy regulations can restrict how customer data is used for verification, potentially weakening the system's ability to distinguish between real and fake users.
• Scalability Challenges – Real-time cross-referencing of every ad request against a massive customer database requires significant computational resources and can be costly to maintain at scale.
• Walled Garden Transparency Issues – Advertisers often have limited visibility into the specific methods and data used for fraud detection, requiring them to trust the retailer's internal reporting without independent verification.

In scenarios involving a high volume of new user acquisition campaigns, a hybrid approach combining the RMN's data with third-party verification tools may be more suitable.

❓ Frequently Asked Questions

How do retail media networks handle fraud for ads shown off the retailer's site?

For off-site ads, RMNs rely on audience matching with their advertising partners. They use the retailer's first-party data to create a clean target audience, but must trust the partner's platform to prevent fraud at the point of impression. This makes off-site campaigns more vulnerable than those on the retailer's own properties.

Can RMNs stop fraud from human click farms?

They are more effective than traditional methods. While a human can mimic browsing, it is very difficult to fake a legitimate purchase history across thousands of accounts. RMNs can identify accounts with excessive ad interaction but no corresponding purchase activity, a strong indicator of click farm behavior.

Does using an RMN guarantee zero ad fraud?

No system is completely immune to fraud. While RMNs significantly lower the risk by leveraging high-quality first-party data, a small amount of sophisticated invalid traffic might still penetrate their defenses, especially from new users who have not yet established a behavioral baseline with the retailer.

Is the fraud detection in a retail media network biased against new shoppers?

This is a recognized challenge. To avoid blocking potential new customers, RMNs often use a tiered approach. Traffic from unknown users is analyzed with other signals, like behavioral heuristics and device integrity, instead of being blocked outright. However, it may be assigned a lower quality score until a history is established.

Why is first-party data so important for fraud detection in this context?

First-party data provides a definitive record of a user's commercial activity with the retailer. Signals like a verified purchase history, loyalty program status, or product return history are nearly impossible for bots to fake at scale, making them high-confidence indicators of a legitimate human shopper.

🧾 Summary

Retail media networks combat digital ad fraud by leveraging their proprietary first-party data, such as customer purchase histories and on-site browsing behavior. This unique access allows them to distinguish real shoppers from fraudulent bots with high accuracy. By verifying ad interactions against actual consumer data, RMNs ensure advertising budgets are spent on legitimate audiences, thereby protecting campaign integrity and improving return on investment.

🔗 Related Articles

• Three Ways to Fight Fraud on Retail Media Networks - https://www.human.security/blog/three-ways-to-fight-fraud-on-retail-media-networks
• DoubleVerify Research Reveals Retail Media is an Opportunity for Safe Engagement Despite Some Viewability Challenges - https://doubleverify.com/newsroom/retail-media-report/
• 3 Steps Retailers Can Take to Detect and Defend Against Ad Fraud - https://www.cmswire.com/customer-experience/3-steps-retailers-can-take-to-detect-and-defend-against-ad-fraud/
• Retail Media Networks Are Making Millions—Here's How You Can Too! | Blog - https://www.everestgrp.com/blogs/retail-media-networks-are-making-millions-heres-how-you-can-too-blog
• Ad Fraud Guide: What It Is, How It Works, And Main Types | Adtelligent - https://adtelligent.com/ad-fraud/