Let's move beyond theory and see how the Integral Ad Science platform performs in the real world.
A Practical Walkthrough: Setting Up Your First IAS Campaign
Deploying an ad verification strategy with IAS is a structured process that goes far beyond simply flipping a switch. The initial and most strategic step is defining your brand's unique suitability framework. This isn't a one-size-fits-all blocklist. It involves working with IAS to classify content into risk tiers—from severe risks like hate speech to moderate risks like controversial news—and deciding your brand's tolerance for each. This nuanced approach ensures you maintain reach while adhering strictly to your brand values. This framework becomes the central logic for all subsequent filtering and measurement.
Once the strategy is set, the technical implementation begins. This involves generating IAS verification tags for your creative assets. These small snippets of code are then implemented within your ad server (like Google Campaign Manager 360) or directly within your Demand-Side Platform (DSP). For pre-bid filtering, you'll activate IAS's curated segments within your DSP's targeting options. These segments, such as 'Fraudulent Traffic - Pre-bid' or 'Brand Risk - High', instruct the DSP to not even bid on impressions that IAS has already flagged as undesirable. This is the most efficient way to prevent waste.
With tags live and pre-bid segments active, you shift to monitoring and optimization within the IAS Signal dashboard. Your initial focus should be on establishing a baseline. Analyze your post-bid reports to understand the level of fraud, brand risk, and low viewability you were previously exposed to. Compare this to the data from your pre-bid segments, which shows how much risk was proactively avoided. This dual perspective provides a powerful narrative about the platform's immediate impact. The final step is ongoing optimization. Use the insights from IAS Signal to refine your strategy—perhaps you can relax certain suitability rules on high-performing sites, or blacklist specific publishers that consistently show high fraud rates. This continuous feedback loop is what turns ad verification from a simple insurance policy into a dynamic performance driver.
- Define Your Brand Suitability Framework. Collaborate with IAS to map content categories to specific risk tiers (low, medium, high, severe) based on your unique brand values, moving beyond generic safety.
- Generate and Implement IAS Tags. Create verification tags in the IAS platform for your specific campaigns and creative assets, then implement them correctly within your ad server or DSP.
- Configure Pre-Bid and Post-Bid Solutions. Activate IAS's pre-bid targeting segments in your DSP to proactively block low-quality inventory. Ensure post-bid monitoring is active on all campaigns to measure what was delivered.
- Analyze Initial Reports in IAS Signal. During the first weeks, focus on establishing a baseline for your media quality. Identify the biggest sources of waste and risk across your media plan.
- Optimize Based on Verification Data. Use the granular, placement-level data to create inclusion/exclusion lists and refine your suitability rules, continuously improving the efficiency of your ad spend.
Don't just set and forget your suitability rules; review them quarterly against performance data.
Feature Face-Off: IAS vs. DoubleVerify vs. MOAT
Choosing an ad verification partner often comes down to a decision between the big three: Integral Ad Science (IAS), DoubleVerify (DV), and Oracle Advertising (formerly MOAT). While all three offer core services in brand safety, fraud, and viewability, they have distinct areas of strength and historical focus. Understanding these differences is key to selecting the right partner for your specific needs. IAS is often lauded for its robust ad fraud Threat Lab and its advanced contextual intelligence capabilities, giving advertisers powerful tools for both avoidance and proactive targeting. DoubleVerify is highly regarded for its deep social media integrations and its Authentic Brand Suitability (ABS) feature, which provides extremely granular controls. MOAT, now part of Oracle, is well-known for its deep focus on attention metrics, going beyond simple viewability to measure user engagement.
The best verification partner isn't the one with the most features, but the one whose data integrates most seamlessly into your optimization workflow.
To make a more informed decision, a direct comparison is helpful. While features are constantly evolving, this table provides a snapshot of their core offerings and differentiators.
| Feature | Integral Ad Science (IAS) | DoubleVerify (DV) | Oracle Advertising (MOAT) |
|---|---|---|---|
| Fraud Detection | Excellent, with a dedicated Threat Lab and strong pre-bid solutions. Very strong in CTV fraud. | Excellent, with a focus on sophisticated invalid traffic (SIVT) and a robust fraud lab. | Strong, with a historical focus on general invalid traffic (GIVT) and bot detection. |
| Brand Suitability | Highly granular, with risk-tier customization and contextual intelligence (sentiment analysis). | Excellent, with Authentic Brand Suitability (ABS) offering page-level analysis and custom categories. | Good, offers standard brand safety categories and keyword blocking. |
| Viewability & Attention | Strong MRC-accredited viewability, with advanced metrics like Time-in-view. | Strong MRC-accredited viewability, with its own 'Authentic Impression' metric. | Market leader in attention metrics, going beyond basic viewability to measure detailed user interaction. |
| CTV Measurement | A key strength, offering comprehensive, MRC-accredited fraud and viewability verification at the app and program level. | Very strong, with a comprehensive solution covering fraud, brand suitability, and viewability in CTV environments. | Good capabilities, but historically seen as catching up to IAS and DV in this specific area. |
| Social Media | Strong integrations with major platforms like TikTok, YouTube, and Meta for viewability and safety reporting. | A key strength, with deep, official partnerships across all major social platforms. | Good integrations, primarily focused on viewability and attention measurement. |
| Reporting Interface | IAS Signal is a unified, user-friendly dashboard with strong trend analysis. | DV Pinnacle is a powerful, highly customizable platform for analysis and reporting. | MOAT Analytics provides deep data on attention, but can be less intuitive than competitors. |
The choice often hinges on your primary channel: for CTV, lean IAS/DV; for deep attention metrics, consider MOAT.
Unpacking the IAS Pricing Model
One of the most common questions about enterprise-level ad verification is, "How much does it cost?" With Integral Ad Science, there isn't a simple, flat-rate subscription. The pricing model is typically based on usage and complexity, ensuring that the cost aligns with the value and scale of the advertiser's media spend. The most common structure is a Cost Per Mille (CPM) fee, where a small charge is applied for every thousand impressions that IAS measures. This CPM fee is not fixed; it's a tiered model that varies based on several factors. The total volume of impressions is a major driver—higher volumes typically secure a lower CPM rate. The specific services used also impact the price. A basic package covering post-bid brand safety and viewability will have a lower CPM than a comprehensive package that includes pre-bid fraud filtering, CTV measurement, and advanced contextual targeting.
Don't view verification as a cost center, but as an investment that makes every other dollar in your media budget work harder.
Furthermore, the environment being measured plays a role. Verifying ads in complex, high-value environments like Connected TV often carries a higher CPM than standard display advertising on the open web, reflecting the increased difficulty and importance of verification in those channels. Because of these variables, pricing is almost always customized. The process involves a consultation with the IAS sales team to understand the advertiser's needs, media plan, and volume, which results in a tailored proposal. While this lack of transparent, off-the-shelf pricing can be frustrating for some, it ensures that brands are only paying for the services they need, scaled to their specific operation.
Negotiate pricing based on total media spend verified, not just impression volume, to better align value.
Who is Integral Ad Science Best For (And Who Should Look Elsewhere?)
Integral Ad Science is not a universal tool for every company that buys digital ads. Its powerful, feature-rich platform is specifically engineered for a certain type of advertiser. The ideal IAS customer is a large, established brand or a media agency managing significant ad spend across multiple digital channels. These organizations are most exposed to the risks of ad fraud and brand damage, and the potential savings from eliminating waste can easily justify the platform's cost. They also have the internal resources—media buyers, ad ops teams, and data analysts—to actively manage the platform and translate its insights into action.
Advertisers with a heavy investment in programmatic advertising and high-growth channels like Connected TV will find IAS particularly valuable. These environments are where the lack of transparency is most acute and the need for third-party verification is greatest. Global brands that need to manage different brand suitability rules across various markets also benefit immensely from the platform's granular controls and scalability. In essence, if your brand's reputation is a core asset and your ad budget is substantial enough that even a small percentage of waste amounts to a significant sum, IAS is a strategic imperative.
Conversely, the platform is likely overkill for small businesses, startups, or advertisers with very limited budgets. For these companies, the cost of IAS's CPM-based fees could be prohibitive, and the complexity might outweigh the benefits. An advertiser running small-scale search campaigns or simple social media ads can often rely on the native brand safety tools provided by platforms like Google Ads and Meta. These built-in controls, while less robust than a third-party solution, offer a sufficient level of protection for smaller-scale operations. The tipping point for considering a solution like IAS is when your media plan diversifies into the open programmatic web and your budget becomes large enough that you can no longer afford to simply trust the default settings.
The tipping point for needing IAS is when your CFO asks to see proof of where ad dollars are going.
Extended FAQ
- How does IAS handle Made-for-Advertising (MFA) sites?
- IAS has specific technology to identify and classify MFA sites, which are low-quality websites designed solely for ad arbitrage. Advertisers can use this classification to exclude MFA inventory from their buys, either through pre-bid filtering or by adding them to a blocklist, thus improving the overall quality and performance of their media.
- What is the typical impact of IAS on campaign performance and CPMs?
- Initially, implementing strict verification can sometimes lead to a slight increase in media CPMs, as you are filtering out cheaper, lower-quality inventory. However, the goal is an improvement in outcomes. By focusing spend on higher-quality, viewable, and fraud-free impressions, advertisers typically see a significant lift in downstream metrics like engagement, conversions, and overall ROI.
- How does IAS integrate with major DSPs like The Trade Desk and DV360?
- IAS has deep, server-to-server integrations with nearly all major DSPs. This allows advertisers to seamlessly activate IAS's pre-bid segments directly within the DSP's targeting interface. This direct integration ensures real-time filtering with minimal latency, making it highly efficient.
- What is "Time-in-view" and why is it a more important metric than standard viewability?
- Standard viewability is a binary, yes/no metric. Time-in-view measures the average duration an ad was visible on the user's screen. It's a much stronger proxy for user attention and the potential for a message to be absorbed. Optimizing for higher Time-in-view often leads to better brand recall and engagement than simply optimizing for basic viewability.
- Can IAS verify ads that run inside mobile applications?
- Yes, IAS offers a comprehensive in-app verification solution. This is typically accomplished by integrating the IAS Software Development Kit (SDK) with the app publisher's own SDK. This allows for the same level of fraud detection, brand safety, and viewability measurement for in-app ads as for mobile and desktop web ads.