Blog

Maximizing ROI: Strategies for Low-Volume Inventory Placements in DV360

November 28, 20233 min readKirti Palwar

Table of contents

In the world of digital marketing, programmatic advertising platforms like DV360 (Display & Video 360) have revolutionized the way advertisers connect with their target audience. However, when it comes to low-volume inventory placements, marketers often face unique challenges. In this article, we will explore the effective strategies for optimizing your digital marketing efforts within DV360 when dealing with low-volume inventory placements.

What is Low-Volume Inventory Placements in Programmatic Advertising?

Low-volume inventory placements refer to situations where there is limited ad inventory available on specific websites, apps, or placements. This scarcity of ad space can be due to various reasons, including niche audiences, limited content, or geographical constraints. Advertisers often find it challenging to allocate budgets effectively and generate meaningful results in such scenarios.

Strategies for Success in Programmatic Advertising

Maximizing Low-Volume Inventory Placements: Navigating the realm of low-volume inventory placements within DV360 demands a strategic approach. Every impression in such scenarios holds significant value, necessitating precise utilization of tools and tactics available within marketing and advertising technology. Here’s a breakdown of strategies tailored to optimize these opportunities:

Precision Targeting through Audience Segmentation: Unlock the potential of DV360’s audience segmentation capabilities. Craft highly specific audience groups by leveraging both first-party and third-party data. The key lies in reaching the right audience with the right message to maximize impact despite limited ad inventory availability.

Dynamic Creative Optimization: Personalization is pivotal. DCO empowers tailoring ad creatives to individual users based on their behaviour, interests, and demographics. Experimentation with diverse ad creatives helps identify the most engaging combinations for your unique audience.

Real-Time Bidding (RTB) and Private Marketplaces (PMPs): RTB within DV360 ensures seizing valuable impressions as they arise in low-volume placements. Additionally, establish private marketplaces with publishers for priority access to limited inventory, particularly effective in niche placements.

Mobile-Centric Approach and Cross-Device Optimization: Given the higher prevalence of low-volume placements on mobile devices, prioritize optimizing ad campaigns for mobile experiences. Implement cross-device targeting to extend reach across platforms and devices, maximizing the chances of locating available inventory.

Continuous Monitoring and Agile Adjustments: The unpredictability of low-volume inventory demands constant vigilance. Regularly monitor campaign performance and dynamically adjust bids, targeting, and creatives based on real-time data insights. Automation via DV360’s rules facilitates instant adjustments under specific conditions.

Strategic Budget Allocation and Bid Strategies: Allocate budgets strategically, focusing on placements likely to yield optimal results. Leverage DV360’s predictive modelling to identify and capitalize on these opportunities. Experimentation with different bid strategies, such as target impression share or target CPA, helps optimize budgetary impact.

Blacklisting Long-Tail Placements for Enhanced Performance: Initiate the process of blacklisting long-tail placements to curb ad fraud and enhance performance. Removing consistently underperforming placements optimizes campaign efficacy and reallocates the budget toward high-performing avenues. For increased efficiency and to reduce the errors of false positives, opt for an ad fraud detection partner like mFilterIt to identify long-tail placements and curb their impact proactively.

Way Forward

In conclusion, while low-volume inventory placements within DV360 pose challenges, they also present lucrative prospects. Harness the potential of targeted audience segmentation, dynamic creative optimization, real-time bidding, and mobile-centric approaches. Stay agile, data-driven, and committed to constant monitoring and adaptation to thrive in this dynamic landscape.

Talk to us

Ready to protect your digital campaigns?

Book a consultation with an mFilterIt specialist and see how we help enterprise brands eliminate ad fraud, protect brand equity, and grow trusted revenue.

Request a Demo

More from the blog

View all

Ad Traffic Validation•July 28, 2026

Everything You Need to Know About Mobile Ad Fraud in 2026

Your mobile app campaigns are delivering installs. But how many of them are real? Or are those installs further converting to genuine user activity or events? The global mobile advertising market reached USD 262.84 billion in 2025 and is expected to grow to USD 322.67 billion in 2026. (Source: Fortune Business Insights) And this is what fraudsters aim to target using sophisticated bot networks, click farms, invalid traffic, and affiliate networks. Because with scalability comes vulnerability if not monitored closely. And there’s a high chance that your mobile app campaigns are being attacked by mobile ad fraud at various stages of the funnel. Another question is, how does it happen, and what mobile ad fraud techniques do fraudsters use? That’s what we are going to talk about in this guide: What is mobile ad fraud? What are the different types of mobile ad fraud techniques? Why is mobile ad fraud increasing in 2026? What are the common signs to identify app fraud? How to detect and prevent ad fraud using app traffic validation solution? Continue reading ahead to learn more. What is Mobile Ad Fraud or Mobile App Fraud? Mobile ad fraud refers to any deliberate manipulation of mobile app campaign data and metrics like impressions, clicks, installs, in-app events, or re-engagements by fraudulent affiliates to earn payouts or claim false credits. Various techniques like click farms, incent fraud, device farms, click injection, etc. are used to generate fake clicks and pass through standard MMP attribution checks. Here’s how it happens inside a campaign: A publisher (an app or site with an audience) shows your ad through an ad network or programmatic exchange. A user sees the ad (an impression) and taps it: a click. That click carries identifiers and is logged by your mobile measurement partner (MMP), If the user installs your app and opens it, the MMP matches the install back to the click, usually crediting the last click before install. That credit triggers a postback, the signal that tells the network and its publishers “this install was yours,” which is what releases the payouts further based on events. From there, the user’s in-app events like registrations, purchases, and deposits get attributed to the same source. Now, if you notice, what holds this whole chain together is trust in signals. The MMP never sees a human being. It sees a click record, an install record, and a timestamp, and infers cause and effect. Whoever controls those signals controls where the money goes. This inference is exactly what fraudsters attack. What are the Various Types of Mobile Ad Fraud Techniques? Here’s how mobile app fraud shows up in your campaigns: Click Fraud Fake clicks, or the credit attached to them. Click Spam: Click flooding at an industrial scale. It is when a huge volume of clicks is sprayed across thousands of devices and campaigns at once, gaming attribution on every install that happens. Click injection: Malware on a device detects that an app install has started and fires a click seconds before first open, stealing credit for an install that was already happening. This is also called the last-click manipulation. Know the difference between click spamming and click injection in detail. Fake Clicks: Clicks with no human intent and bot-generated clicks inflate billable CPC counts, plus click farms (rooms of low-paid workers or racks of phones physically tapping ads). Fake Attribution: This is also known as attribution hijacking. The umbrella term for claiming credit for installs and conversions that another source (or no source at all) actually drove. Distribution Fraud The spread of environment signals is itself the tell. Outdated Operating Systems: Improbable concentration or mismatch in OS data. Internet Service Providers: Traffic unnaturally clustered on one carrier, or inconsistent with the claimed geo. Device-Level Fraud: Fraudsters manipulate device identities by spoofing or repeatedly resetting advertising IDs, making a single device appear as hundreds of unique users. Suspicious device-model patterns and recurring IDs expose this activity. Device Fraud An install fraud technique that brings users with no genuine interest. Fake installs are the install-stage rung between click and engagement. Every method of faking one betrays itself through device signals, which is why they all live here. Fake Device: Device spoofing (faking make/model/OS) + emulator farms (thousands of virtual devices on one server, no hardware required). Duplicate User: One device posing as many: reset/fabricated advertising IDs where the same IDs keep resurfacing; device/install farms cycling real phones through reset identities; and reward abuse (gaming referral bonuses and first-order coupons by becoming a “first-time user” repeatedly via cloned apps and farmed numbers). APK Fraud: Tampered/repackaged builds and forged app-side signals; SDK spoofing (forged install signals sent straight from a fraudster’s machine, no device, no install, no user) fits here. Incorrect Region: Device-level geo that contradicts the target market. Device fraud is very prominent in case of affiliate marketing. Learn more with examples here. IP Fraud Mobile ad fraud that disguises what and where the traffic is coming from. VPN and proxy traffic: Masking true location so traffic from anywhere appears to come from your target geography, where payouts are higher. Data-center traffic: Clicks and installs originating from server farms, not phones. The bluntest signal there is that real users don’t live in data centers. Affiliate Fraud In 2026, the affiliate ecosystem is where the largest share of mobile ad fraud actually lives. Not because affiliates are inherently dishonest, but because the channel’s structure gives affiliate fraud everything it needs: Opacity through re-brokering: Campaigns pass through chains of networks and sub-publishers; by the time your ad runs, you’re several hops from the source. Volume-rewarding payouts: CPI/CPA models pay for outcomes, so any affiliate who can fake the outcome cheaper than earning it has a direct incentive. Incent fraud: Fraudulent affiliates take your campaign and run it on offer/incent walls for pennies per install (“install, register, keep the app for 2 days”), pocketing the gap between your CPI and the user’s reward. The installs are real; the interest is zero; engagement dies the moment the reward clears. Vanishing affiliates: They re-register under new IDs; without transaction-level evidence, clawing back payouts is nearly impossible. Blended traffic: Fraud arrives mixed with legitimate traffic from the same network, so averages look healthy while individual sub-publishers run 30%+ fraud. We have a detailed guide on everything you need to know about affiliate fraud. Check it out. If affiliates drive a meaningful share of your growth and you’re not independently validating that traffic, assume you’re leaking budget. Which Campaign Metrics Get Manipulated Because of Mobile Ad Fraud? Almost every metric you report to your leadership team. Mobile ad fraud doesn’t just waste your budget; it distorts campaign performance, making it difficult to understand what’s actually working. CTR (Click-Through Rate): Fake clicks generated by bots or click farms artificially inflate CTR, making ads and placements appear more engaging than they really are. CVR (Conversion Rate): Click

Blog•July 24, 2026

How To Navigate Brand Safety in 2026? Key Considerations To Implement

Did you know? 15% of ad spend gets wasted on unsafe inventory. Brands lose 30% ROI because of brand safety incidents. 75% of consumers avoid brands with unsafe ads. Brand recall reduces by 25% on unsafe placements. (Source: Gitnux) That’s the cost of brand safety risks when not taken care of on time. Brand safety has become a growing concern for digital advertisers and marketers in 2026. Synthetic, AI-generated content, deepfakes, misinformation, disinformation, and wrong messaging are being spread across large volumes all over the internet. As online fraudsters become more creative and innovative with the techniques they use to commit fraud, and as AI reshapes what “content” even means online, advertisers are also becoming more cautious. If you are one such advertiser, this article is for you. In the upcoming sections, we will discuss how advertisers and brands can prepare themselves to face and triumph over the ever-evolving brand safety issues in 2026, including the biggest structural shift the industry has seen in years. Without wasting any time, let’s jump right in. Why is Brand Safety Essential for Brands in 2026? Brand safety is important to every single stakeholder involved in the advertising supply chain. Starting from the ad networks to the publishers, it is important to do their part in maintaining brand safety to ensure that their customers continue trusting them. It helps ensure your brand ads are always aligned with the values your brand carries and does not show up against Paying attention to brand safety and protection isn’t just important from the point of view of preventing a negative impact on brand reputation. There are many positive benefits to actively pursuing brand safety and monitoring. For a Positive Brand Image Brand safety threats take many forms, from ads appearing on websites hosting extremists or questionable content to placements alongside sites engaging in fraudulent activity. Either can seriously damage a brand’s reputation. While advertisers often have little control and transparency over exact ad placements, consumers don’t see it that way. They can easily assume a brand endorses the views or activities on the site its ad appears next to. This isn’t a hypothetical risk. The world’s biggest brands have faced this exact issue and used their scale to push some accountability onto publishers like Google. Smaller brands don’t have that leverage, making proactive brand safety monitoring essential rather than optional. To Make a Strong First Impression Established brands have history and loyal audiences to fall back on, which helps them recover faster from a brand safety incident. New and emerging brands don’t have that cushion. A single incident can be far more damaging early on, and even in less severe cases, it often drives up advertising costs, since rebuilding a reputation from scratch usually demands a bigger-than-usual ad spend. The Post-GARM Reality: Why the Old Brand Safety Methods No Longer Work For years, brand safety conversations leaned heavily on one shared reference point: the Global Alliance for Responsible Media (GARM), a cross-industry body that gave advertisers a common taxonomy for “unsafe” content. That one-size-fits-all approach doesn’t hold up in 2026. Content moves faster; formats have multiplied, and risk looks different for every brand. A fixed, generic keyword list cannot keep pace. What this means practically for advertisers: there is no more shared checklist to lean on. Many teams have shifted to a “dirty dozen” list of content categories as a baseline, then customized it to their own risk tolerance, which is exactly the approach outlined in the “Key Considerations” section below. Static keyword blocklists, long treated as a safety net, have also proven unreliable and are increasingly seen as a legacy tool rather than a real safeguard. They tend to over-block legitimate publishers while still missing genuinely unsafe placements. The net effect is that brand safety in 2026 depends far more on real-time, contextual, AI-driven analysis than on any fixed industry list, which is precisely the gap a dedicated brand safety solution is built to close. The New Frontier: AI-Generated Content and Ad Adjacency If misinformation and bot traffic defined the brand safety conversation in 2023, AI-generated content adjacency defines it in 2026. This is a genuinely new category of risk, not a rebrand of an old one: Over one in five videos recommended by platform algorithms are now estimated to be AI-generated, often low-quality “slop,” per analysis of social platform data. However, the real risk isn’t AI content itself. Its low-quality, unlabeled AI content sitting next to a brand ad without any way to tell the two apart. It is also because of this that disclosure is now emerging as an important theme within the industry. The IAB’s framework for AI Transparency & Disclosure, published in early 2026, advocates that advertisers use machine-readable metadata to disclose the use of AI in advertisements, and most members of Gen-Z and millennial generations believe that transparent disclosure of the use of AI would positively influence their purchasing intentions. Key Considerations to Implement Brand Safety in 2026 Now that we have established the importance of ensuring brand safety, you may be wondering what you can do to ensure the same. Here are some key considerations to keep in mind to ensure your brand’s reputation is not under threat: SetBrandSafety Guidelines Developing guidelines for brand safety best practices will allow you to learn what actions you have to undertake (or avoid) in order to ensure the reputation of your brand. In general, most advertisers try to stay away from any content referring to war, drugs, weaponry, crime, death, and hate-speech. Although such categories can be considered a good foundation for developing the list of unsafe content for your brand, keep in mind that it is not a complete one. Create and Proactively Update a List of Blocklisted Sites Once you have identified what type of content you want to avoid, it is time to identify websites that are known to publish such content. This will be your own list of blocklisted websites. You can instruct publishers to never publish your content on any website in this list and ensure your brand does not appear next to questionable content. To ensure that this exercise has an effect, it is necessary to recognize that this is not just a one-off effort. Static blocklists age quickly and may not detect new threats, especially those such as content farms generated using artificial intelligence technology. It is advisable that you keep on revisiting this list. Monitor andManage AIContent Adjacency AI-generated content

Ad Fraud•July 22, 2026

Top 3 Cost Benefits of Using Ad Fraud Solution in BFSI Sector

Among a plethora of challenges faced by a brand and a marketer, ad fraud makes it to the top of the battle list. The battle is real where bots are eating up the digital ad budgets and burning a hole in the effectiveness of ad spending. What’s the outcome? The advertising has no effect on your business and money spent on ads goes down the drain or the bots which means that brands and marketers pay the fraudsters one out of every four times when an ad has been clicked. Not really the most effective way to spend money on advertising, right? So where and how does an ad fraud solution help your business? In the BFSI sector, the number of transactions and requests that must be looked at and analyzed are in millions. Here, the most prominent cost is the cost per lead because the conversion value in BFSI is of high, cost per lead becomes high automatically. It is no surprise that their marketing departments are becoming a hide-out for fraudsters cashing out the ad budgets through lead fraud. With high incentives and minimal chances of being caught, these fraudsters are feasting off the spending while the businesses deal with the loss. Here we list the top 3 cost benefits of using an ad fraud solution to make the most out of your ad spend. Best Ad Fraud Detection Solution: Top 3 Cost Benefits Meanwhile, your ads might be created with the right intent, backed by solid creative and precise audience targeting. But the moment they enter the digital ecosystem; they step into territory you no longer fully control. This is why brands need more than surface-level reporting. They need genuine transparency and visibility into who is filling the lead forms and how many of these leads are actually qualified. That’s precisely where an ad fraud solution helps brands fight mobile ad fraud, Following are the top 3 cost benefits of an ad fraud solution: Reduce Customer Acquisition Costs with High-Quality Leads A fake lead isn’t a single wasted click, it triggers a chain of downstream costs. Bots and click farms submit form fills that look legitimate, so sales reps spend hours chasing people who were never real, CRMs fill with junk records, and the numbers meant to show marketing performance stop reflecting reality. Ad fraud detection filters invalid traffic before it reaches the funnel, so only genuinely interested people convert into leads. Sales and marketing spend their time on real opportunities instead of cleaning up after fraud. Business Impact Lower cost per acquired customer Sales capacity spent on real prospects rather than dead records Cleaner, more trustworthy CRM data Eliminate Budget Waste Caused by Fraudulent Campaign Optimization Ad platforms optimize toward whoever converts. When fraudulent form fills get counted as conversions, the algorithm “learns” that fraudulent users are your best audience and pushes more budget toward the exact sources producing fake leads. Left unchecked, the fraud compounds itself: the more fake leads come in, the more spend gets steered toward generating even more of them. Ad fraud detection flags invalid conversions before they feed the optimization signal, so platforms train on real buyers instead of bots. Your media budget gets pulled back out of the fraud loop and pointed at genuine demand. Business Impact Ad algorithms optimize toward real converters, not bots Less budget redirected into fraudulent traffic sources Lower cost per qualified lead Maximize Marketing ROI Across Every Advertising Dollar Fraudsters repeatedly submit lead forms using the same phone number, email, or device ID — or slightly altered versions of each — to manufacture the appearance of high engagement. These duplicate “punched” leads inflate campaign performance and skew attribution, making it nearly impossible to tell a genuinely winning campaign from a manipulated one. An Ad fraud detection identifies repeated identifiers, synthetic patterns, and duplicate submissions before they reach your reports. With clean data flowing into your marketing and CRM systems, you get an accurate read on what’s actually working — the foundation for smarter budget allocation and forecasting. Business Impact Accurate campaign attribution and reporting Better media budget allocation across channels Higher confidence in forecasting and performance metrics Conclusion Every marketer wants the same outcome; higher-quality leads, lower acquisition costs, and stronger returns from every campaign. But achieving that starts with knowing how much of your ad spend is actually reaching real customers. An ad fraud detection solution gives you that visibility, helping you optimize campaigns with confidence instead of assumptions. Book a demo with mFilterIt to uncover hidden ad fraud in your campaigns and see how you can turn more of your ad spend into measurable business growth. Frequently Asked Questions What is ad fraud in BFSI marketing? Ad fraud happens when fake users or bots click on ads for BFSI brands. This means that BFSI brands have to pay for clicks, views or leads that’re not real. Why do BFSI brands need ad fraud detection? The cost of getting one customer is very high for BFSI brands. Ad fraud detection helps get rid of website visitors makes sure the people who are interested in the brand are real and saves the marketing budget. How does ad fraud detection reduce customer acquisition costs? Ad fraud detection stops people and fake leads from using up the marketing budget. This means that the money spent on ads is only used on people who are really interested in the BFSI brand, which lowers the cost of getting new customers. Can ad fraud detection improve lead quality? Yes it can. Ad fraud detection removes useless leads before they even get to the sales team. This results in leads and more people actually becoming customers. How does ad fraud impact campaign performance? When there is ad fraud it skews the campaign results, which leads to decisions wasted money and lower returns on the marketing investment, for BFSI brands.