The Mobile App Install Cost Crisis Nobody's Talking About

Mobile app marketers face a brutal reality in 2025: average Cost Per Install (CPI) rates have climbed to $2.65-$3.50 on Google Ads, with some categories like finance hitting $8.70 per install. Yet most advertisers focus exclusively on creative optimization and bid strategies while ignoring the most powerful lever for reducing waste—advanced negative targeting. The difference between basic exclusions and sophisticated negative targeting frameworks can reduce your CPI by 40% or more, transforming unprofitable campaigns into scaling machines.

Most app marketers implement only the most obvious exclusion: existing users. But that's table stakes. The hidden opportunity lies in the layers of negative targeting that filter out low-intent placements, fraudulent traffic sources, and non-converting user segments before they drain your budget. These advanced tactics require a different mindset—moving from reactive optimization to proactive traffic quality control.

With paid app installs projected to reach $94.9 billion by the end of 2025, the stakes have never been higher. Whether you're managing a portfolio of mobile apps for an agency or scaling your own product, understanding these hidden negative targeting levers isn't optional—it's the difference between sustainable growth and burning through venture capital on junk installs.

Why Basic User Exclusions Aren't Enough Anymore

Every app marketer knows to exclude existing users from install campaigns. You upload your customer lists, integrate your Mobile Measurement Partner (MMP), and call it done. But this baseline approach leaves massive gaps in your targeting defense system. The reality is that existing user exclusions address only 10-15% of potential wasted spend in modern app campaigns.

The remaining 85-90% of waste comes from sources that basic exclusions can't touch: low-quality placements on mobile apps, fraudulent traffic networks, users in geographies where your app doesn't support payments, devices running outdated operating systems, and behavioral segments that install but never engage. Each of these sources bleeds your budget while inflating your CPI metrics and destroying your return on ad spend.

Google Ads App campaigns use machine learning to automatically distribute your ads across Search, Play, YouTube, and the Display Network. This automation delivers scale, but it also removes your ability to manually control where ads appear. Without sophisticated negative targeting at the account and campaign level, you're essentially giving Google a blank check to show your ads anywhere it predicts a conversion—and Google's definition of a "quality install" rarely matches yours.

Lever 1: Mobile App Placement Exclusions That Actually Work

The Google Display Network includes millions of mobile apps, and by default, your app install campaigns are eligible to show ads within these apps. Users playing mobile games or checking weather apps see your ads—generating plenty of clicks but almost zero quality installs. According to Google's official placement exclusion documentation, advertisers can exclude up to 65,000 placements per account to prevent wasted spend on low-performing inventory.

The nuclear option is to exclude all mobile app placements entirely using the placement exclusion mobileappcategory::69500 or adding adsenseformobileapps.com to your account-level exclusion list. This single action can immediately cut 30-50% of wasted clicks from your campaigns, particularly if you're seeing high click-through rates but abysmal conversion rates.

However, blanket exclusions sacrifice potential high-quality traffic from premium apps. A more sophisticated approach involves creating tiered exclusion lists based on your placement performance data. Export your placement report monthly, identify apps with install rates below 0.5% or cost-per-install above 3x your target, and add them to a shared exclusion list that applies across all campaigns. This iterative process compounds over time, building an institutional knowledge base of placements to avoid.

For Performance Max campaigns running app installs, placement exclusions require additional steps. You cannot add campaign-level placement exclusions directly in the interface. Instead, use Google's Performance Max Campaign Modification Request Form to submit up to 65,000 placement exclusions per campaign. This workaround adds friction, but it's essential for controlling traffic quality in Google's most automated campaign type.

Manage your exclusion lists strategically using Google Ads' shared library. Create multiple themed lists: one for fraudulent apps, one for game apps, one for entertainment apps with poor conversion history. Manager accounts can create up to 3 placement exclusion lists with 250,000 exclusions per list and apply them across client accounts. This tiered governance approach ensures consistent quality standards across your entire app portfolio.

Lever 2: Geographic Exclusions Based on Monetization Data

Not all installs carry equal value, yet most app marketers treat global traffic equally. The harsh truth: a user in a Tier 3 country might cost you $0.50 to acquire but generate $0.10 in lifetime value, while a U.S. user costs $4.00 but delivers $25.00 in LTV. Geographic negative targeting based on actual monetization data is the fastest path to improved unit economics.

CPI varies dramatically by geography. According to industry research on global CPI benchmarks, U.S. traffic remains expensive due to high monetization potential, while Tier 1 Western countries (Canada, France, Germany, UK) offer cheaper CPI with strong economies of scale. But the real insight comes from your own data: which countries deliver profitable users versus which deliver install volume that never monetizes?

Start by segmenting your install base by country and calculating actual 30-day LTV by cohort. Any country where LTV:CAC ratio falls below 1.5:1 becomes a candidate for exclusion or dramatic bid reduction. For apps with in-app purchase models, exclude countries without payment infrastructure for your category. For subscription apps, exclude regions where credit card penetration is below 30% and you don't support local payment methods.

Implement a testing framework before making permanent geographic exclusions. Create a separate campaign targeting your potential exclusion countries with 10% of your budget. Run it for 30 days, track not just install volume but Day 7 retention, Day 30 retention, and revenue per user. If the data confirms poor unit economics, exclude those geos from your primary campaigns and reallocate budget to proven markets. This controlled testing approach ensures you're making data-driven decisions rather than assumptions.

Don't treat geographic exclusions as permanent. User behavior and monetization patterns shift seasonally. A country that performs poorly in Q1 might show dramatically improved metrics during Q4 holiday shopping. Review your geographic exclusion strategy quarterly, retest excluded regions with small budget allocations, and adjust based on current performance data rather than outdated assumptions.

Lever 3: Device and Operating System Version Targeting

One of the most overlooked negative targeting opportunities in app campaigns is excluding users on outdated devices and operating system versions. If your app requires iOS 15+ or Android 10+ to function properly, why pay to acquire users on older versions who will have terrible experiences and immediately uninstall?

Google Ads allows device targeting by model and operating system version. Create exclusion lists for iOS versions below your minimum supported version and Android versions that represent less than 5% of your active user base or show install-to-activation rates below 40%. This prevents spending on users who literally cannot use your app or will encounter so many bugs they churn within hours.

Beyond OS versions, segment by device quality. Premium apps with subscription models should exclude budget Android devices where users historically show poor willingness to pay. Conversely, apps targeting price-sensitive audiences should exclude premium devices where your offering appears too basic compared to native alternatives. This device-based behavioral segmentation can improve your effective CPI by 15-25% by aligning user acquisition with monetization probability.

Analyze your MMP data or Firebase Analytics to identify device models with anomalous behavior: unusually high install rates but zero engagement, suspiciously identical session patterns suggesting emulator farms, or device models popular in fraud networks. These signals indicate non-human traffic or incentivized installs that inflate your numbers without delivering real users. Add these device models to your exclusion lists immediately.

For apps available on both iOS and Android, resist the temptation to apply identical targeting across platforms. iOS users on average generate 3x the revenue of Android users, justifying higher CPIs. Your Android campaigns should employ more aggressive device and OS exclusions to maintain profitability, while iOS campaigns can afford slightly looser targeting given higher user value. This platform-specific approach recognizes the fundamental economic differences between app stores.

Lever 4: Behavioral Audience Exclusions You're Missing

Beyond demographic and device targeting, Google Ads offers behavioral audience targeting based on user interests, in-market signals, and affinity categories. Most marketers focus on positive targeting—adding audiences they want. But negative audience targeting creates equally powerful results by excluding users whose behavior patterns predict poor fit with your app.

If you're in a competitive app category, consider excluding users who recently installed competing apps. While this sounds counterintuitive, users who just downloaded your main competitor are in the trial phase with that product and unlikely to simultaneously adopt yours. They represent wasted impressions and clicks during their competitor evaluation period. Wait 60-90 days, then re-target them if they haven't converted to paying customers on the competitor platform.

Exclude affinity audiences whose interests fundamentally misalign with your app's value proposition. A premium meditation app should exclude audiences interested in "free apps" or "budget lifestyle." A hardcore gaming app should exclude casual mobile game audiences. These exclusions prevent your ads from reaching users whose stated preferences predict low conversion probability and poor retention.

Create sophisticated remarketing exclusion lists based on engagement signals. Exclude users who visited your app store listing but didn't install within 7 days—they've seen your value proposition and declined. Exclude users who installed but uninstalled within 24 hours—they represent fundamental product-market misfit. Exclude users who installed, opened once, and never returned—they're unlikely to respond to re-acquisition messaging. This remarketing exclusion discipline prevents burning budget on audiences who've already voted with their behavior.

If you're running app campaigns across multiple platforms (Google, Meta, Apple Search Ads), create a unified audience exclusion strategy. Users who didn't convert on Facebook after seeing 10+ ads are unlikely to convert on Google. Export your non-converting audiences from each platform and import them as exclusions on others. This cross-platform negative targeting prevents the same unqualified users from cycling through your entire paid media budget.

Lever 5: Fraud Prevention Through Smart Exclusions

Mobile app install fraud represents a $7+ billion problem industry-wide. Fraudsters use device farms, emulators, click injection, and SDK spoofing to generate fake installs that look real in your attribution dashboard but deliver zero actual users. Traditional fraud detection happens post-install, but strategic negative targeting prevents fraudulent traffic from ever entering your funnel.

While Google Ads doesn't allow direct IP exclusion, you can exclude geographic regions known for high concentrations of device farms and click farms. Certain cities in specific countries show suspiciously high install volumes with zero corresponding engagement. Work with your MMP to identify geos where install volumes exceed 100 per day but Day 1 retention is below 5%. These anomalies indicate systematic fraud and warrant immediate geographic exclusion.

Analyze your placement data for fraud signatures: individual apps or websites delivering hundreds of installs at impossibly low CPIs with zero post-install engagement. These placements often use incentivized traffic (users paid to install apps) or bot networks. The placement exclusion methodology should include automatic flagging of any placement that delivers 50+ installs with zero Day 7 retention, triggering immediate exclusion pending manual review.

Tighten your attribution windows to combat click flooding attacks. If you're using a 7-day click attribution window, fraudsters can inject clicks before organic installs and steal attribution credit. For app campaigns, consider reducing to a 1-day click window for paid search placements and 3-day for display. While this may lower your reported conversion volumes, it provides more accurate attribution and reduces fraud vulnerability.

Work with your fraud prevention partner to identify SDK spoofing patterns—fake install events that never happened. These won't show in Google Ads as clicks but will appear in your MMP as installs, creating a mysterious gap between Google's reported conversions and your actual install counts. When this gap exceeds 10%, investigate whether specific campaigns, placements, or geos are disproportionately affected, then apply exclusions accordingly.

Build fraud prevention into your campaign governance structure. Require weekly fraud audits comparing install counts to engagement metrics. Any campaign showing cost-per-engaged-user more than 5x higher than cost-per-install likely contains significant fraud or extremely poor targeting. Pause these campaigns, analyze traffic sources, add aggressive exclusions, and relaunch with tighter controls. This systematic approach transforms fraud prevention from reactive cleanup to proactive quality control.

Lever 6: Negative Keywords for App Campaign Search Inventory

Many app marketers assume negative keywords don't apply to App campaigns since Google automates keyword targeting. This is partially true—you can't add positive keywords. But you absolutely should add negative keywords to prevent your ads from showing on irrelevant searches that Google's algorithm mistakenly targets.

As of March 2025, Google increased the negative keyword limit for Performance Max campaigns from 100 to 10,000 per campaign. This dramatic expansion acknowledges that even highly automated campaigns benefit from human oversight on traffic quality. For app campaigns running across Search inventory, strategic negative keywords prevent waste on informational queries, competitor brand names, and searches indicating intent incompatible with your app.

Build a comprehensive list of informational modifiers: "how to," "what is," "tutorial," "guide," "review," "comparison," "vs," "alternative," "free," "crack," "hack." Users searching these terms are researching, not ready to install. They're in the awareness stage, not the conversion stage. Excluding these modifiers can reduce your wasted search spend by 20-30% while barely impacting install volumes since these queries rarely convert anyway.

Add competitor app names as negative keywords unless you have a specific competitive conquest strategy with dedicated creative. Generic app install campaigns showing on competitor brand searches perform poorly because users searching for a specific competitor are unlikely to install your app instead. Save your budget for non-branded searches where users are still evaluating options. This competitor keyword discipline prevents head-to-head battles you're unlikely to win.

For any app category, exclude job-seeking terms: "jobs," "career," "hiring," "apply," "work," "employment," "salary," "resume." These searches represent users looking for employment at companies in your industry, not users looking to install your product. A fintech app showing on "fintech jobs" searches wastes money on completely mismatched intent.

Treat your app campaign negative keyword list as a living document that grows with your campaign intelligence. Start with a foundation of 200-300 obvious exclusions, then add 10-20 new terms weekly based on search term report analysis. Use systematic negative keyword building methodologies to ensure you're capturing patterns, not just individual terms. Over six months, this compound growth creates a proprietary exclusion list containing 800-1,000 terms that dramatically improve your traffic quality.

Lever 7: The Creative Asset and Exclusion Connection

Your creative assets directly influence which searches and placements trigger your ads. A headline emphasizing "Free Trial" attracts users searching for free solutions—exactly the audience you should exclude if you're running a paid app. The relationship between creative messaging and negative targeting is symbiotic: better creative reduces the need for exclusions by naturally attracting qualified users, while strategic exclusions amplify creative effectiveness by ensuring it reaches the right audiences.

Audit your creative assets for unintended signals that attract low-quality traffic. Words like "easy," "simple," and "free" in headlines trigger Google's algorithm to show your ads to bargain-hunters and casual users rather than serious buyers. If your app requires paid subscription, remove "free" language entirely from ad copy and add it as a negative keyword. This alignment between messaging and exclusions creates consistency that improves conversion quality.

Google's machine learning uses engagement signals from your creative assets to determine placement quality. If your video ads generate high completion rates on premium YouTube channels but poor performance on game apps, Google should learn this pattern. But you can accelerate this learning by proactively excluding placements where your creative format performs poorly. Portrait video performs well in mobile app feeds but terribly on desktop YouTube placements. Landscape creative works for YouTube but wastes money on Instagram Stories inventory. Match your creative format to placement opportunities and exclude mismatches.

Use exclusion performance data to inform creative testing priorities. If you're excluding hundreds of game app placements due to poor performance, that's a signal that casual mobile gamers don't resonate with your value proposition. Test creative variations that speak to non-gaming audiences and measure whether they improve performance on remaining placements. Conversely, if you're successfully targeting productivity apps, double down on creative that emphasizes efficiency, time-saving, and professional benefits.

For apps running in multiple languages and regions, create region-specific exclusion lists that account for cultural nuances. A term that's perfectly neutral in English might carry negative connotations in another language, attracting wrong audiences. Work with native speakers to identify culturally-specific exclusions for each major market. This localization depth separates sophisticated app marketers from those running one-size-fits-all global campaigns with mediocre results everywhere.

Measuring the Impact: Beyond Surface-Level CPI

Cost Per Install is a vanity metric if those installs don't engage with your app. The real measure of negative targeting effectiveness is Cost Per Engaged User (CPEU)—what you pay to acquire a user who completes your core activation event. For a meditation app, that's completing the first session. For a fintech app, that's linking a bank account. For a gaming app, that's reaching level 5.

Implement cohort analysis that tracks users acquired before and after implementing advanced negative targeting. Create cohorts for each major exclusion implementation: pre-mobile app exclusions, post-mobile app exclusions, pre-geographic exclusions, post-geographic exclusions. Track 30-day retention, 30-day revenue per user, and LTV:CAC ratio for each cohort. The difference reveals your true ROI from negative targeting investments.

Run incrementality tests to measure whether negative targeting actually improves unit economics or just shifts credit to remaining traffic. Create geo-holdout tests where you implement aggressive negative targeting in 70% of countries but maintain baseline targeting in 30%. Compare total install volumes, engagement rates, and revenue per user across test and control groups. If negative targeting is working, test groups should show lower install volumes but dramatically higher quality metrics and better overall ROI.

Use attribution path analysis to understand how negative targeting affects the customer journey. Users excluded from app install campaigns might still convert through other channels—search campaigns, direct app store visits, organic social. Track whether your negative exclusions reduce attributed conversions but increase overall conversion quality across all channels. This holistic view prevents over-optimizing one channel while harming overall acquisition efficiency.

Build negative targeting into your regular reporting framework. Include metrics for exclusion list size, percentage of impressions filtered, estimated waste prevented, and quality score improvements. Create dashboards that make negative targeting visible to stakeholders rather than buried in campaign settings. When leadership sees that your exclusion lists prevented $50,000 in wasted spend last quarter, you'll get budget to invest in more sophisticated targeting tools and strategies.

Automation and AI: Scaling Negative Targeting Intelligence

Manual negative targeting works for small accounts, but it doesn't scale. When you're managing dozens of app campaigns across multiple apps, spending hours weekly reviewing placement reports and adding exclusions becomes unsustainable. The future of negative targeting lies in AI-powered automation that learns patterns and implements exclusions faster than humans can.

Start with rules-based automation: scripts that automatically flag placements meeting specific criteria for exclusion. Any placement generating 50+ clicks with zero installs gets automatically added to an exclusion list. Any geographic region showing install-to-activation rate below 10% triggers an alert for manual review. These simple rules handle 70% of obvious optimization opportunities without requiring daily manual analysis.

More sophisticated approaches use machine learning for pattern recognition. AI systems can identify that game apps in the "casual puzzle" category universally underperform for your B2B productivity app, even before each individual game accumulates enough data for statistical significance. These pattern-based exclusions prevent waste during the learning phase rather than after you've already spent thousands of dollars testing placements that were predictably poor fits.

The most advanced automation uses contextual analysis similar to what powers Negator.io for search term classification. Instead of relying purely on performance metrics, AI analyzes the context of placements, device signals, and behavioral patterns to predict traffic quality before showing ads. A placement might look promising based on category and audience, but contextual signals—time of day patterns, session duration anomalies, geographic concentration—reveal it's likely fraud or incentivized traffic. Excluding these placements proactively prevents waste rather than cleaning up after the fact.

The optimal approach combines AI automation with human strategic oversight. Let automation handle the tactical work—identifying obvious exclusions, flagging anomalies, implementing rules-based filters. Humans focus on strategic decisions—which patterns matter, how aggressive to set thresholds, when to test re-including previously excluded segments. This division of labor maximizes efficiency while maintaining strategic control over campaign quality.

Integrate your negative targeting automation with your broader marketing technology stack. Your MMP should feed engagement and revenue data into your exclusion decision engine. Your fraud prevention tool should automatically trigger exclusions for placements showing fraud signatures. Your analytics platform should surface cohort performance data that informs geographic and behavioral exclusions. This ecosystem approach ensures negative targeting decisions are based on comprehensive data rather than siloed campaign metrics.

Your 90-Day Implementation Roadmap

Implementing advanced negative targeting doesn't happen overnight. A structured 90-day rollout ensures you're making methodical improvements while measuring impact at each stage. This phased approach prevents overwhelming your campaigns with simultaneous changes that make it impossible to isolate what's working.

Days 1-30: Foundation and Quick Wins

Begin with a comprehensive audit of your current state. Export 90 days of placement data, geographic performance, device performance, and search term data (if available). Identify the bottom 20% of placements by install rate and the bottom 20% by cost-per-engaged-user. These become your initial exclusion candidates—low-hanging fruit that will show immediate impact.

Implement mobile app category exclusions if you haven't already. This single action typically delivers the fastest improvement in traffic quality. Monitor the impact for two weeks, tracking changes in install volumes, install rates, and cost-per-install. Expect install volumes to drop 10-20% but cost-per-install to improve by 15-25%, resulting in net efficiency gains.

Build your foundational negative keyword list of 200-300 terms covering informational modifiers, job-seeking terms, and obvious low-intent searches. Add these to all app campaigns running search inventory. Review search term reports weekly and add 10 new negative keywords based on actual query data.

Days 31-60: Behavioral and Geographic Refinement

Analyze 60 days of post-install engagement data by geography. Calculate LTV:CAC ratios by country, segmenting into profitable (LTV:CAC > 3:1), marginal (1.5:1 to 3:1), and unprofitable (< 1.5:1) tiers. Exclude unprofitable geos entirely. Create separate campaigns for marginal geos with lower bids and tighter targeting. Concentrate 70% of budget on proven profitable geos.

Implement device and OS version exclusions based on your app's technical requirements and historical performance data. Exclude OS versions below your minimum supported version. Exclude device models showing activation rates below 50% or fraud signatures. Monitor impact on install quality metrics over three weeks.

Create behavioral audience exclusions for users who previously engaged with your brand but didn't convert. Build remarketing lists for app store visitors who didn't install, installers who uninstalled within 24 hours, and users who installed competitor apps in the last 30 days. Apply these as negative audiences across acquisition campaigns.

Days 61-90: Automation and Systematic Scaling

Implement automation rules or scripts that codify your exclusion criteria. Set up automatic flagging for placements meeting poor performance thresholds. Create scheduled reports that surface exclusion candidates weekly rather than requiring manual digging through interface reports.

Audit creative assets for messaging that attracts wrong audiences. Test creative variations that emphasize your actual value proposition (premium quality, professional tools, serious results) rather than generic benefits (easy, simple, free). Measure whether refined creative reduces the need for exclusions by naturally attracting better-fit users.

Establish comprehensive measurement frameworks that track negative targeting impact. Create dashboards showing exclusion list growth, estimated waste prevented, cohort quality improvements, and ROI attribution to negative targeting initiatives. Present these results to stakeholders to justify continued investment in targeting sophistication.

Common Pitfalls and How to Avoid Them

The biggest risk in aggressive negative targeting is over-exclusion: being so restrictive that you eliminate viable traffic sources and cap your growth potential. This typically happens when marketers set exclusion rules based on short-term data or small sample sizes. Require statistical significance before permanent exclusions—at least 100 installs or 30 days of data for any placement or geo before making final decisions.

Negative targeting isn't a set-and-forget strategy. User behavior evolves, Google's algorithm changes, new placements launch, and your app's value proposition shifts. Review your exclusion lists quarterly. Test re-including previously excluded segments with 5-10% budget allocations to see if performance has improved. Markets that were unprofitable six months ago might be viable now due to product improvements or market maturation.

Optimizing purely for lowest CPI often leads to poor long-term results. You can drive CPI to $0.50 by targeting only developing markets with no payment infrastructure, but those users will never generate revenue. Always optimize for quality-adjusted CPI or directly for cost-per-engaged-user and LTV:CAC ratio. These metrics force you to consider the full funnel rather than just top-of-funnel vanity numbers.

Different app campaign types require different negative targeting approaches. Standard App campaigns allow more control than Performance Max campaigns. Search campaigns require negative keyword focus while display campaigns need placement exclusions. YouTube campaigns benefit from channel exclusions while Discovery campaigns need audience exclusions. Don't apply one-size-fits-all exclusion strategies across fundamentally different campaign types.

Negative targeting changes attribution patterns in ways that can confuse analysis. When you exclude low-quality placements, users who would have clicked those placements might still convert through other touchpoints, making it look like you reduced conversions. Use view-through conversion windows carefully, implement incrementality testing, and focus on overall business metrics (total revenue, total profit) rather than just campaign-attributed conversions.

The Compound Advantage: Why Starting Today Matters

Every day you run app campaigns without sophisticated negative targeting, you're leaving 20-40% efficiency gains on the table. But more importantly, you're not building the institutional knowledge that compounds over time. An exclusion list built over six months becomes a proprietary asset—a database of traffic quality intelligence that new competitors can't replicate quickly.

In a market where average CPIs are climbing year over year, the apps that master negative targeting create a sustainable competitive advantage. While competitors burn budget testing placements you already know don't work, you're concentrating spend on proven channels at lower effective CPIs. This efficiency gap compounds: you can bid more aggressively on quality traffic, acquire users faster, generate better reviews, improve organic rankings, and further reduce your dependency on paid acquisition.

Consider the math: if you're spending $50,000 monthly on app install campaigns at an average $3.00 CPI, you're acquiring 16,667 installs. Implementing the negative targeting strategies in this guide can realistically reduce your effective CPI to $1.80 (40% improvement) without reducing quality. That's 27,778 installs for the same budget—an additional 11,111 users monthly. Over a year, that's 133,332 additional users acquired with zero additional budget. If your average LTV is $10, you've generated an incremental $1.33 million in lifetime value from targeting optimization alone.

The hidden negative targeting levers aren't actually hidden—they're just overlooked by marketers focused on bidding strategies and creative testing. Start with the foundation: mobile app placement exclusions and informational query negative keywords. Build to intermediate tactics: geographic segmentation and device targeting. Advance to sophisticated strategies: behavioral audiences, fraud prevention, and creative-exclusion alignment. Automate the tactical execution while maintaining strategic human oversight. And most importantly, measure everything so you can prove ROI and justify continued investment in targeting excellence.

Your competitors are still excluding only existing users and calling it done. You now have the framework to implement seven distinct negative targeting levers that work together to cut CPI by 40% while improving user quality. The question isn't whether these strategies work—the data proves they do. The question is whether you'll implement them before or after your competitors discover these same hidden levers. In mobile app marketing, that timing difference determines who scales profitably and who burns through funding chasing vanity metrics. Choose wisely.