Moat – Ad Viewability, Fraud Detection, and Brand Safety

Overview

Moat, an Oracle product, is an ad verification and measurement platform designed for advertisers, agencies, and publishers. Its primary function is to provide independent third-party validation of digital ad campaign performance, focusing on key metrics such as viewability, invalid traffic (IVT), and brand suitability. Established in 2009 and acquired by Oracle in 2017, Moat has developed a suite of tools that integrate across various ad environments, including display, video, mobile, and connected TV (CTV) advertising.

The platform’s core offering, Moat Analytics, delivers granular data on ad impressions, allowing users to understand if ads were actually seen by human users and for how long. This capability is critical in a programmatic advertising ecosystem where media quality can vary significantly. Moat’s methodology for viewability measurement is accredited by the Media Rating Council (MRC), ensuring adherence to industry standards for impression counting and viewability metrics Moat's MRC accreditation details. Beyond viewability, Moat also provides tools for detecting sophisticated invalid traffic, including bot activity and ad fraud, which can inflate impression counts and diminish campaign effectiveness.

For brand advertisers, Moat offers brand safety and suitability features, enabling them to control the context in which their ads appear. This includes preventing ads from running alongside inappropriate content and ensuring alignment with brand values. The platform supports cross-platform measurement, providing a unified view of ad performance across different devices and channels. Developers can integrate Moat data and capabilities into their existing systems via APIs, allowing for custom reporting, automated workflows, and deeper analysis within proprietary dashboards. This focus on verifiable metrics and data integration positions Moat as a tool for optimizing media spend and enhancing accountability in digital advertising.

Moat's target audience includes large enterprises, media agencies, and publishers who require robust, independent verification of their ad campaigns. Its capabilities are particularly valuable in complex programmatic setups where transparency into ad delivery and audience engagement is paramount. For example, a global brand running campaigns across multiple demand-side platforms (DSPs) can use Moat to standardize viewability and IVT reporting, ensuring consistent performance measurement. This allows for data-driven decisions on media optimization, such as adjusting bids based on viewability scores or reallocating budgets to higher-performing inventory sources. The platform serves as a critical component in maintaining trust and efficiency within the digital advertising supply chain.

Key features

• Ad Viewability Measurement: Provides metrics on whether an ad was actually seen by a user, including the percentage of pixels on screen and duration, across various formats (display, video, mobile, CTV).
• Invalid Traffic (IVT) Detection: Identifies and filters out non-human or fraudulent traffic, such as bot activity and ad stacking, to ensure that impressions are delivered to legitimate users.
• Brand Safety and Suitability: Enables advertisers to define and enforce content classifications to prevent ads from appearing next to inappropriate or brand-damaging content, aligning with advertiser-defined suitability guidelines.
• Cross-Platform Ad Measurement: Offers unified reporting and analytics across different digital channels and devices, providing a holistic view of campaign performance.
• Attention Metrics: Beyond viewability, Moat tracks user engagement signals like scroll speed and interaction rates to provide deeper insights into ad effectiveness.
• Programmatic Integrations: Connects with various demand-side platforms (DSPs), supply-side platforms (SSPs), and ad exchanges to facilitate seamless measurement and optimization.
• Reporting and Analytics: Provides customizable dashboards and detailed reports for analyzing ad performance data, with options for exporting data for further analysis.
• API Access: Offers programmatic interfaces for integrating Moat data into third-party systems, custom dashboards, and workflow automation tools, as described in their Oracle Moat Analytics documentation.

Pricing

Moat operates on a custom enterprise pricing model. Specific costs are determined based on factors such as the volume of impressions measured, the specific suite of products utilized (e.g., Moat Analytics, Moat Measurement, Moat Reach, Moat Outcomes), and the scope of services required. Prospective clients typically engage directly with Oracle’s sales team to obtain a tailored quote that aligns with their advertising measurement needs.

Common integrations

• Google Marketing Platform (GMP): Integration with Google Ads, Display & Video 360, and Campaign Manager 360 for consistent measurement across Google's ecosystem.
• Meta (Facebook/Instagram): Provides measurement capabilities for campaigns running on Meta's advertising platforms, as detailed in Meta's measurement guides.
• The Trade Desk: Integrates with The Trade Desk DSP for viewability and IVT measurement in programmatic ad buys.
• Magnite (Rubicon Project/Telaria): Works with SSPs like Magnite to provide publishers with insights into ad quality and inventory performance.
• X (formerly Twitter): Supports ad measurement for campaigns on the X platform.
• TikTok: Offers measurement solutions for TikTok ad campaigns, helping advertisers verify performance on the platform TikTok's measurement partner list.
• Snapchat: Integrates for ad verification and viewability measurement on Snapchat's ad network.
• Amazon Advertising: Provides measurement for ads served through Amazon's advertising platforms, including Sponsored Products and Amazon DSP.
• Custom Integrations: Through its APIs, Moat allows for custom data integration with proprietary systems, data warehouses, and business intelligence tools.

Alternatives

• Integral Ad Science (IAS): Offers similar ad verification services, including viewability, fraud, and brand safety, with a strong focus on media quality and contextual targeting.
• DoubleVerify: Provides comprehensive ad verification and measurement solutions, specializing in media authentication, fraud prevention, and brand safety across digital channels.
• Comscore: A media measurement and analytics company that offers solutions for ad validation, audience measurement, and campaign effectiveness across platforms.
• Ad ऑडिटर (by Google): While not a direct competitor in the same third-party verification sense, Google Ad Manager and Google Ads have built-in measurement tools.

Getting started

Integrating Moat for ad measurement typically involves implementing Moat's JavaScript tags or SDKs into ad creative or publisher pages, or configuring integrations directly within a DSP or ad server. For developers accessing Moat data programmatically, the process involves API authentication and making requests to retrieve specific metrics. Below is a conceptual example of how a developer might interact with a hypothetical Moat API endpoint to retrieve viewability data for a specific campaign, assuming an authenticated session.

import requests
import json

# NOTE: This is a conceptual example. Actual Moat API endpoints and authentication
# will vary and require valid credentials and specific query parameters as per
# Oracle Moat's developer documentation.

MOAT_API_BASE_URL = "https://api.moat.com/v1/"
API_KEY = "YOUR_MOAT_API_KEY" # Replace with your actual API key

HEADERS = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

# Example: Get viewability data for a specific campaign ID
def get_campaign_viewability(campaign_id, start_date, end_date):
    endpoint = f"campaigns/{campaign_id}/viewability"
    params = {
        "startDate": start_date,
        "endDate": end_date,
        "metrics": "viewable_impressions,measurable_impressions,viewability_rate"
    }
    try:
        response = requests.get(MOAT_API_BASE_URL + endpoint, headers=HEADERS, params=params)
        response.raise_for_status() # Raise an HTTPError for bad responses (4xx or 5xx)
        return response.json()
    except requests.exceptions.HTTPError as errh:
        print(f"Http Error: {errh}")
    except requests.exceptions.ConnectionError as errc:
        print(f"Error Connecting: {errc}")
    except requests.exceptions.Timeout as errt:
        print(f"Timeout Error: {errt}")
    except requests.exceptions.RequestException as err:
        print(f"Something Else: {err}")
    return None

if __name__ == "__main__":
    # Example usage
    sample_campaign_id = "123456789"
    sample_start_date = "2026-04-01"
    sample_end_date = "2026-04-30"

    print(f"Fetching viewability data for Campaign ID: {sample_campaign_id} from {sample_start_date} to {sample_end_date}")
    data = get_campaign_viewability(sample_campaign_id, sample_start_date, sample_end_date)

    if data:
        print("Successfully retrieved data:")
        print(json.dumps(data, indent=2))
    else:
        print("Failed to retrieve viewability data.")

This Python snippet illustrates how to construct an API request to a hypothetical Moat endpoint. In a real-world scenario, developers would consult the Oracle Moat Analytics documentation for exact API specifications, authentication methods (e.g., OAuth 2.0), available endpoints, and required parameters. The implementation typically involves setting up secure API credentials, handling responses, and parsing the JSON data to integrate into dashboards or data analysis pipelines.