The Enterprise Challenge: Managing Negative Keywords Across 100+ Accounts

When you're managing 100+ Google Ads accounts, negative keyword management transforms from a tactical task into a strategic infrastructure problem. What works for 5 accounts breaks completely at 50. What works at 50 becomes unmanageable at 100. The complexity doesn't scale linearly—it compounds exponentially. Every account you add multiplies the combinations of campaigns, ad groups, and search term patterns you need to monitor.

According to Google's official MCC documentation, a single manager account can handle up to 85,000 non-manager accounts. But technical capacity and operational reality are vastly different. Most agencies hit a management wall long before reaching platform limits. The challenge isn't whether Google's infrastructure can handle the volume—it's whether your team's processes can scale without breaking down.

Without proper architecture, you face a painful reality: search term waste multiplies across every account. That 15-30% waste rate you see in individual accounts doesn't average out at scale—it compounds. One hundred accounts each wasting $500 monthly equals $50,000 in pure waste. The manual approach that worked when you had 10 clients becomes impossible when managing enterprise-level account portfolios.

Understanding Shared Negative Keyword List Architecture

Shared negative keyword lists are Google Ads' native solution for applying the same exclusions across multiple campaigns. Instead of manually adding "free," "jobs," or "DIY" to every single campaign, you create one list and apply it universally. This sounds straightforward—and for small account portfolios, it is. But at enterprise scale, you need architecture patterns that go far beyond basic shared lists.

Google's 2025 platform updates significantly expanded shared list capabilities. According to Google's shared negative keyword documentation, each account can now create up to 20 negative keyword lists with 5,000 keywords each, and apply a single list to 1,000 campaigns simultaneously—up from the previous 200 campaign limit. For Performance Max campaigns specifically, Google increased the negative keyword limit from 100 to 10,000 keywords by March 2025, giving advertisers unprecedented control.

These expanded limits matter enormously for enterprise management. With 20 lists per account at 5,000 keywords each, you theoretically have capacity for 100,000 negative keywords per account. Multiply that across 100+ accounts, and you're managing millions of potential exclusions. The platform can handle it—but can your governance model?

The MCC-Level Shared Library Advantage

The most powerful—and underutilized—feature for enterprise management is the MCC-level shared library. When you create negative keyword lists in your manager account, they're automatically added to the shared library of all client accounts by default. This creates a centralized control point where a single list update propagates across every connected account instantly.

This architecture solves a critical problem: version control at scale. Without MCC-level lists, you'd need to update the same negative keyword across 100 separate accounts manually. With one person managing that change, you're looking at 2-3 hours of work. With MCC-level control, that same update takes 30 seconds. The time savings alone justify the architectural investment, but the real value is consistency—ensuring every account benefits from the same optimization simultaneously.

To implement this effectively, navigate to your MCC account, access Tools and Settings, then Shared Library, and select Exclusion Lists. Any negative keyword list created here becomes available across all child accounts. The key is developing a naming convention and governance structure that makes these shared lists discoverable and maintainable as your account portfolio grows.

The Three-Tier Architecture Pattern for Enterprise Scale

Managing 100+ accounts requires a hierarchical architecture that balances standardization with flexibility. The most effective pattern we've seen across high-performing agencies is the three-tier model: Universal, Vertical, and Account-Specific negative keyword lists. This pattern provides governance without rigidity, allowing strategic control while maintaining the agility to handle unique client needs.

Tier 1: Universal Exclusion Lists

Your universal tier contains negative keywords that apply across every account, every campaign, every industry. These are the search terms that will never—under any circumstances—convert for any client. Think "free," "jobs," "careers," "salary," "resume," "download," "torrent," "hack," and similar junk traffic that wastes budget universally.

Build your universal tier in categorical lists for better organization and maintenance. Create separate lists for employment terms, educational queries, DIY researchers, illegal activities, and competitor brand names. According to industry research on enterprise PPC management patterns, this categorical approach reduces maintenance time by 40-50% compared to monolithic lists because you can audit and update specific categories without affecting others.

Implement these universal lists at the MCC level, automatically applying them to all new accounts as they're added to your portfolio. This creates a baseline protection layer that every account inherits immediately. Your universal tier typically contains 500-2,000 negative keywords distributed across 5-8 categorical lists. This foundational layer prevents the most obvious waste without requiring account-specific configuration.

Tier 2: Vertical-Specific Negative Keyword Lists

The vertical tier handles industry-specific exclusions that apply across all accounts within a particular business category. If you manage 30 e-commerce clients, 25 B2B SaaS companies, and 45 local service businesses, you need separate vertical-specific negative keyword architectures for each group.

For e-commerce clients, you might exclude "wholesale," "bulk order," "supplier," or "manufacturer" if they only serve retail customers. For B2B SaaS accounts, exclude "consumer," "personal use," "individual," or "student" if they target enterprise buyers. For local service providers, exclude geographic terms outside their service areas plus competitor business names in their region.

The vertical tier requires more active maintenance than universal lists because industry search patterns evolve. New competitors enter the market. Search trends shift. Product categories emerge. Plan for quarterly reviews of vertical-specific lists, analyzing search term reports across all accounts in each vertical to identify patterns worth excluding. Scaling negative keyword management from one account to 50+ with an MCC requires this systematic vertical-tier approach to maintain efficiency as your portfolio grows.

Tier 3: Account-Specific Negative Keywords

The account-specific tier handles unique exclusions that only apply to individual clients. These are the negative keywords driven by specific business models, geographic restrictions, product limitations, or brand positioning that don't generalize across other accounts—even within the same vertical.

A luxury watch retailer might exclude "affordable," "budget," and "cheap," while a discount watch seller actively targets those terms. A national home services franchisor might need different geographic exclusions for each franchise territory. A software company might exclude legacy product names they've sunset but competitors still offer.

Account-specific lists typically live at the individual account level, not in the MCC shared library. This gives account managers autonomy to make client-specific optimizations without requiring approval chains or risking unintended impacts on other accounts. However, you should still maintain naming conventions and documentation standards so these account-specific decisions remain visible and auditable across your organization.

Implementing Governance for 100+ Account Management

Architecture without governance fails at scale. You need clear ownership, approval workflows, and change management processes that prevent well-intentioned optimizations from creating unintended consequences across your account portfolio.

Ownership Structure and Role Definition

Define clear ownership for each tier of your architecture. Universal lists should be owned by your head of paid search or PPC director—someone with strategic oversight across the entire portfolio. Vertical lists might be owned by vertical leads or senior account managers who specialize in specific industries. Account-specific lists remain the responsibility of individual account managers but with visibility requirements for quality assurance.

Establish approval chains for changes at each tier. Universal list changes require director-level approval because they impact every account. Vertical list changes might need approval from the vertical lead plus one senior peer reviewer. Account-specific changes can often be implemented by account managers autonomously, but with post-implementation documentation and spot-check reviews.

Change Management and Testing Protocols

Never apply sweeping negative keyword changes across 100+ accounts without testing. Implement a staged rollout protocol: test changes in 5-10 representative accounts first, monitor for 7-14 days, analyze impact on impressions and conversions, then gradually expand to additional accounts if results validate the change.

Build rollback procedures into your governance model. Document every shared list change with date, owner, rationale, and expected impact. If a negative keyword accidentally blocks valuable traffic, you need the ability to identify the change quickly and reverse it across all affected accounts. This is particularly critical for MCC-level changes that propagate automatically to child accounts. The 3-tier negative keyword governance model provides a comprehensive framework for managing these enterprise-level controls without sacrificing agility.

Documentation and Knowledge Transfer

At enterprise scale, institutional knowledge becomes critical infrastructure. When an account manager leaves and takes three years of vertical-specific negative keyword decisions with them, you lose valuable optimization intelligence. Implement documentation standards that capture not just what negative keywords exist, but why they were added and what impact they had.

Use your project management system, shared drives, or dedicated PPC documentation tools to maintain a negative keyword decision log. For each significant addition—especially at the vertical tier—document the search term that triggered the decision, the accounts affected, the expected waste reduction, and the actual impact after 30 days. This creates an institutional learning system that improves over time rather than resetting with every team change.

Automation Patterns for Managing Shared Lists at Scale

Manual management fails beyond 50 accounts. At 100+ accounts, automation isn't optional—it's structural. You need systems that identify negative keyword opportunities, apply them consistently, and monitor impact without requiring constant human intervention.

Cross-Account Search Term Aggregation

The core challenge at scale is visibility. Individual account managers reviewing search term reports in isolation miss patterns that only emerge when you aggregate data across the entire portfolio. A search term that appears once in each of 50 accounts might not trigger action at the account level but represents significant waste in aggregate.

Implement automated search term aggregation that pulls data from all accounts in your MCC, normalizes the data, and identifies high-frequency irrelevant queries across the portfolio. Google Ads Scripts can handle this for smaller portfolios, but beyond 50 accounts, you'll need more robust solutions. The Google Ads API allows you to build custom aggregation tools, or you can leverage specialized platforms designed for multi-account management.

AI-Powered Search Term Classification

Human review of search terms doesn't scale past a certain point. If you're managing 100 accounts that each generate 500 unique search queries monthly, you're looking at 50,000 search terms to evaluate. Even at 5 seconds per term, that's 70+ hours of pure classification work—before you've made a single optimization.

AI-powered classification systems analyze search terms in context, using your business profile, active keywords, and conversion data to determine relevance automatically. Instead of reviewing 50,000 terms manually, you review the 500 terms the AI flags as uncertain, approving or rejecting its recommendations. This reduces analysis time by 95% while maintaining human oversight for edge cases. Research on negative keyword management efficiency shows that AI optimization platforms typically deliver 30-45% overall efficiency gains through this comprehensive approach, compared to 20-30% from manual optimization alone.

Protected Keyword Safeguards

The biggest risk in automated negative keyword management is accidentally blocking valuable traffic. At enterprise scale, this risk multiplies. One overly aggressive negative keyword applied across 100 accounts can eliminate thousands of valuable impressions before you notice the problem.

Implement protected keyword lists that prevent automation from excluding terms related to core products, services, or target audiences. If you manage accounts for companies selling "free shipping" as a feature, you need "free" on your protected list despite it being universal junk traffic for most advertisers. Protected keywords create guardrails that let automation work aggressively on obvious waste while preventing catastrophic mistakes on nuanced terms.

Tools like Negator.io power internal agency workflows by combining AI classification with protected keyword safeguards, allowing you to scale negative keyword management across unlimited accounts without risking valuable traffic exclusion. This architectural approach—automation with safeguards—is essential for managing 100+ accounts efficiently.

Workflow Implementation Patterns for Enterprise Teams

Architecture and governance mean nothing without effective workflows that your team actually follows. At enterprise scale, you need workflows that distribute responsibility appropriately, provide clear action paths, and create accountability without micromanagement.

The Weekly Enterprise Optimization Cycle

Implement a weekly optimization cycle that balances systematic review with operational efficiency. Monday: automated systems aggregate search term data from all accounts and surface high-priority opportunities. Tuesday: vertical leads review opportunities within their specialties and approve recommended additions to vertical-specific lists. Wednesday: approved changes deploy to staging test accounts. Thursday-Friday: monitor impact and address any issues before weekend. This structured cycle ensures consistent optimization without becoming all-consuming.

Prioritize review time based on account size and waste potential. Your largest accounts generating the most search query volume deserve proportionally more attention. Smaller accounts can be managed through automated rules and exception-based review. This tiered attention model ensures you're optimizing where it matters most rather than treating every account equally regardless of budget or complexity.

Exception-Based Management for Scale

You cannot actively manage 100+ accounts with equal attention—the math simply doesn't work. Instead, implement exception-based management where accounts only demand attention when they deviate from expected patterns. Set automated alerts for accounts showing unusual search term waste, sudden impression drops (indicating overly aggressive negatives), or conversion rate changes that might signal negative keyword issues.

Define clear thresholds that trigger exception reviews. For example: any account where irrelevant search terms exceed 25% of total impressions, any account showing 15%+ impression decline week-over-week, or any account where protected keywords appear in negative lists. These exceptions surface automatically, allowing your team to focus attention where problems exist rather than reviewing accounts that are performing as expected.

Quarterly Strategic Portfolio Reviews

Weekly optimization handles tactical additions, but you need quarterly strategic reviews to assess whether your overall architecture remains fit for purpose. As your account portfolio grows and evolves, the vertical categories that made sense at 50 accounts might need restructuring at 100. New patterns emerge. Industry dynamics shift. Competitive landscapes change.

During quarterly reviews, pull aggregate data across all accounts in each vertical. Identify the most common negative keywords added at the account-specific level over the past quarter. If you see the same negative keywords repeatedly added across 20+ accounts in a vertical, that's a signal to promote those terms to your vertical-specific tier. This creates a learning loop where account-level insights gradually improve vertical-level architecture, making your entire system smarter over time. Structuring negative keyword workflows for multi-client accounts requires this balance between tactical execution and strategic evolution.

Technical Implementation Considerations and Platform Limitations

Understanding Google Ads platform limitations prevents architectural decisions that look good theoretically but fail in practice. At 100+ account scale, you'll encounter platform constraints that don't appear in smaller implementations.

API Rate Limits and Batch Operations

If you're using the Google Ads API for automation, you're subject to rate limits that affect how quickly you can make changes across large account portfolios. Standard access allows 15,000 operations per day per developer token. At enterprise scale, you may need to request higher rate limits or architect your automation to respect these constraints through batch operations and strategic timing.

Structure your API operations in batches that align with your weekly optimization cycle. Instead of attempting to update all 100+ accounts simultaneously, process them in waves throughout the week. This spreads API load, reduces risk of hitting rate limits, and provides natural checkpoints where you can verify changes before proceeding to the next batch.

Change Propagation and Latency Considerations

When you update a shared negative keyword list at the MCC level, changes don't propagate to all child accounts instantaneously. Google's systems typically process these changes within 2-4 hours, but under heavy load or during peak optimization periods, delays can extend to 12-24 hours. This latency affects your change management protocols—you cannot make an MCC-level change and immediately verify its impact across all accounts.

Build verification steps into your workflow that account for propagation delays. After making significant shared list changes, wait 24 hours before assessing impact. Pull data from a representative sample of accounts to verify that changes have fully propagated before declaring success or rolling back. This patience prevents premature conclusions based on incomplete data.

Performance Max Campaign Specific Considerations

Performance Max campaigns presented unique challenges for enterprise negative keyword management until Google's 2025 updates. The expansion from 100 to 10,000 negative keywords per campaign fundamentally changed how agencies approach PMax optimization at scale. However, even with expanded limits, Performance Max negative keywords operate differently than Search campaign negatives, requiring adjusted strategies.

Performance Max negative keywords only apply to Search inventory within the campaign, not to Display, YouTube, or other placement types. This means your negative keyword strategy must account for this limitation—you're not fully controlling where ads appear across all channels, only within Search placements. For enterprise portfolios heavily invested in Performance Max, this partial control requires supplementary strategies like placement exclusions and audience targeting refinements.

Real-World Implementation: 150-Account Agency Architecture

Theory becomes practical when you see it implemented. One enterprise PPC agency managing 150 Google Ads accounts across e-commerce, B2B SaaS, and professional services verticals rebuilt their negative keyword architecture using the three-tier pattern in early 2025. The results validate the architectural approach.

The Before State: Manual Chaos

Prior to restructuring, the agency managed negative keywords primarily at the account level. Each account manager maintained their own lists with minimal standardization. Universal junk terms like "free" or "jobs" were added repeatedly across accounts as new team members onboarded clients. No formal process existed for sharing learnings across accounts. The agency estimated 15-20 hours weekly were spent on duplicate negative keyword work across the team.

Aggregate search term waste across the portfolio averaged 22% of total impressions—meaning more than one in five ad impressions were completely irrelevant. For a portfolio generating 50 million monthly impressions, that represented 11 million wasted impressions monthly. Even at modest CPCs, this translated to $80,000-$120,000 in monthly waste across the entire portfolio.

The Implementation Process

The agency implemented the three-tier architecture over a 12-week period. Weeks 1-3: audit existing negative keywords across all accounts and categorize them into universal, vertical, and account-specific groupings. Weeks 4-6: build and test universal and vertical shared lists in the MCC, applying them to pilot accounts in each vertical. Weeks 7-9: roll out shared lists across the full portfolio in waves, monitoring for unintended impacts. Weeks 10-12: train team on new governance model and workflows, documenting processes and establishing quarterly review cycles.

The project required approximately 120 total hours of senior PPC strategist time spread across the three-month implementation. They used a combination of Google Ads Scripts for data extraction, spreadsheet analysis for categorization, and manual implementation of the MCC-level shared lists. One technical challenge emerged around Performance Max campaigns, which required separate handling due to the platform's negative keyword limitations at the time—this would be easier with 2025's expanded PMax negative keyword capabilities.

Results and Performance Impact

Within 90 days of full implementation, aggregate search term waste declined from 22% to 11%—a 50% reduction. This translated to approximately $45,000-$60,000 in monthly waste eliminated across the portfolio. Client ROAS improved an average of 18% across accounts that had previously shown above-average waste. Account managers reported that the shared lists handled approximately 70% of negative keyword needs automatically, freeing up time for strategic work rather than repetitive exclusion lists.

The team's weekly time investment in negative keyword management dropped from 15-20 hours to 6-8 hours—a 60% reduction. More significantly, this reduced time delivered better results because effort shifted from duplicate manual work to strategic vertical-tier optimization and exception-based account-level refinement. The agency scaled from 150 to 180 accounts over the subsequent six months without adding PPC headcount, citing the efficiency gains from architectural improvements as a key enabling factor.

Client retention improved as account performance became more consistent across the portfolio. Previously, negative keyword management quality varied based on individual account manager diligence and expertise. The three-tier architecture ensured every client benefited from universal best practices regardless of which team member managed their account. This consistency reduced performance variance and improved client satisfaction scores. The agency now uses their negative keyword architecture as a selling point when pitching new enterprise clients, demonstrating systematic optimization capabilities that smaller competitors cannot match.

Advanced Architecture Patterns for Specialized Scenarios

The three-tier model handles 80% of enterprise scenarios effectively, but specialized situations require architectural variations. Understanding these advanced patterns helps you adapt the framework to unique portfolio characteristics.

Geographic Segmentation Architecture

Agencies managing multi-location businesses or international accounts need geographic segmentation built into their architecture. A national retailer with 200 store locations requires location-specific negative keywords that exclude competitor names and geographic terms outside each store's trade area. At scale, this cannot be managed account-by-account.

Implement regional shared lists within your vertical tier. Create separate negative keyword lists for major geographic regions—Northeast, Southeast, Midwest, Southwest, West for US-based campaigns, or country-specific lists for international portfolios. These regional lists contain competitor names, geographic exclusions, and localized terminology that varies by region. Apply regional lists based on campaign geographic targeting, ensuring each campaign inherits appropriate regional exclusions automatically. Managing 50+ client accounts without burning out your PPC team often requires this type of geographic segmentation to maintain relevance across diverse markets.

Seasonal and Temporal Pattern Architecture

Some negative keywords should only apply during specific time periods. Retailers might exclude "Black Friday" during most of the year but actively target it in November. B2B companies might exclude "conference" terms except during major industry event seasons. Event-based businesses need date-specific negative keyword management that changes based on their event calendar.

Create dated shared lists that activate and deactivate based on calendar schedules. Instead of manually adding and removing seasonal negatives, build separate shared lists for each season and establish a calendar-based workflow where these lists are applied or removed from campaigns at scheduled intervals. This requires more sophisticated automation—Google Ads Scripts can handle the scheduling, applying and removing shared lists from campaigns based on date ranges you define. Document seasonal patterns and continuously refine seasonal lists based on year-over-year learnings.

Competitive Intelligence Integration Architecture

As your portfolio grows, competitive intelligence becomes increasingly valuable for negative keyword strategy. Understanding which competitor terms waste budget across multiple accounts helps you build more effective vertical-specific lists. But competitive landscapes change—new competitors emerge, others exit the market, brand names change through acquisitions.

Implement automated competitive monitoring that feeds your negative keyword architecture. Use tools that track competitor presence in your clients' auctions, identify new competitor brands appearing in search term reports across your portfolio, and flag competitor terms consuming significant impression share. Build competitor-specific negative keyword lists within your vertical tier that update dynamically as competitive intelligence identifies new threats. This proactive approach prevents waste from new competitors before they drain significant budget across your portfolio.

Measurement, Optimization, and Continuous Improvement

Architecture without measurement is guesswork. You need clear metrics that demonstrate whether your shared list strategy is working and where it needs refinement. At enterprise scale, aggregate metrics matter as much as account-level performance.

Key Performance Indicators for Shared List Effectiveness

Track aggregate search term waste percentage across your entire portfolio. Calculate total irrelevant impressions divided by total impressions monthly. This portfolio-wide metric shows whether your architecture is effectively reducing waste at scale. Set targets based on industry benchmarks—15% or less is excellent, 15-20% is acceptable, above 20% indicates architectural gaps. Break this metric down by vertical to identify which industry groups need additional attention.

Measure time investment in negative keyword management as a percentage of total PPC management hours. This efficiency metric demonstrates whether your architecture is delivering the promised time savings. Track both absolute hours and hours per account managed. As your portfolio grows, hours per account should decline if your architecture scales effectively. If time investment per account remains constant or increases, your architecture isn't scaling.

Monitor coverage metrics that show what percentage of accounts benefit from each tier of your architecture. Universal lists should apply to 100% of accounts by definition. Vertical lists should cover 100% of accounts within their designated industries. Calculate what percentage of total negative keywords come from each tier—this reveals whether you're over-indexing on account-specific additions (indicating vertical tier gaps) or successfully managing most exclusions through shared lists.

Attribution and Impact Measurement

Measuring the specific impact of negative keyword changes across 100+ accounts requires careful attribution methodology. Implement before-and-after analysis for significant shared list additions, comparing 14 days before the change to 14 days after across all affected accounts. Control for external factors by comparing affected accounts against similar accounts that didn't receive the change when possible.

Focus attribution analysis on impression volume changes, click-through rate improvements, and conversion rate impacts. Negative keywords should reduce impressions on irrelevant queries while maintaining or improving CTR and conversion rates on remaining traffic. If you see impression declines accompanied by CTR or conversion rate declines, you may have blocked valuable traffic and need to refine your additions or add terms to protected keyword lists.

Building Continuous Learning Systems

Your architecture should get smarter over time, learning from every search term decision across your portfolio. Implement feedback loops that capture which negative keywords deliver the biggest impact, which cause problems, and which patterns emerge repeatedly across accounts. Use this intelligence to continuously refine your universal and vertical tiers.

Create a centralized knowledge base that documents high-impact negative keyword discoveries. When an account manager finds a search term pattern that's wasting budget across their accounts, capture that pattern and test whether it applies to other accounts in the same vertical. Build a suggestion engine that recommends negative keywords to account managers based on patterns found in similar accounts. This institutional learning system ensures your architecture becomes more effective as your portfolio grows rather than becoming more complex and unwieldy.

Conclusion: Your Implementation Roadmap

Managing negative keywords across 100+ Google Ads accounts without systematic architecture is unsustainable. The manual approach that works at small scale breaks completely at enterprise level, creating waste that compounds across your portfolio and consuming time that should focus on strategic optimization rather than repetitive exclusion lists.

The three-tier architecture pattern—universal, vertical, and account-specific negative keyword lists—provides the structure needed to scale effectively. Combined with governance models that define ownership and approval chains, automation that handles classification and application, and workflows that balance systematic optimization with exception-based management, this approach reduces waste by 40-60% while cutting negative keyword management time by similar margins.

Start your implementation with these steps: First, audit your current negative keyword landscape across all accounts, categorizing existing negatives into universal, vertical, and account-specific groups. Second, build and test your MCC-level universal shared lists, applying them to pilot accounts before full rollout. Third, develop vertical-specific lists based on industry patterns you've identified through the audit. Fourth, implement governance and workflow changes that align with the new architecture. Fifth, measure results and refine continuously based on performance data.

Expect 12-16 weeks for complete implementation across a 100+ account portfolio. The initial investment delivers returns almost immediately through reduced duplicate work and captured waste, with compounding benefits as your architecture matures and institutional learning improves list quality over time. Agencies that have implemented this architecture report that it's one of the highest-ROI operational improvements they've made, both for client results and internal efficiency.

At enterprise scale, operational excellence becomes competitive advantage. Smaller agencies cannot match the systematic optimization capabilities that proper architecture enables. Clients paying for enterprise-level management expect enterprise-level processes. Your negative keyword architecture—often invisible to clients—directly impacts the consistency and quality of results you deliver across your entire portfolio. Building this infrastructure demonstrates the professionalism and sophistication that differentiates top-tier agencies from tactical campaign managers.

The difference between managing 100+ accounts successfully and struggling under that weight often comes down to architecture. Stop fighting manual processes that cannot scale. Build systems that get smarter as they grow larger. Your clients, your team, and your bottom line will all benefit from the investment in proper negative keyword infrastructure.