Google Ads Advanced Strategy Ideas

Introduction

The paid advertising landscape has fundamentally shifted. Where once manual optimization and bid management could deliver competitive advantage, today's Google Ads ecosystem demands a more sophisticated approach--one that harmonizes artificial intelligence with strategic human oversight, data-driven decision-making with creative excellence.

This guide explores advanced strategies that separate high-performing campaigns from average ones. We'll examine how to structure accounts for scalability, leverage automated bidding without losing control, optimize for Quality Score at a deep level, and build testing frameworks that compound improvements over time. These aren't basic optimizations--they're strategic approaches that require understanding of how Google's algorithm thinks, how your data tells a story, and how to balance automation with intentionality.

The goal isn't just more clicks or conversions--it's building a paid search operation that improves continuously, scales predictably, and delivers genuine business growth. Whether you're managing enterprise-level accounts or looking to elevate mid-market campaigns, these advanced strategies provide the framework for sustained performance. For organizations facing rising costs, our guide on combatting rising CPCs provides complementary cost-optimization tactics.

Our paid advertising services combine data-driven methodology with hands-on expertise to transform campaigns from cost centers into growth engines.

The Foundation: Account Structure for Scale

Why Structure Determines Outcomes

Campaign structure is often treated as an administrative concern, but it fundamentally shapes what the algorithm can and cannot do with your campaigns. When campaigns are too broad, automated bidding lacks the signals it needs to make intelligent decisions. When structure is too fragmented, data becomes diluted and optimization becomes guesswork.

The principle guiding advanced account structure is segmentation with purpose. Every division in your account should exist because it serves a strategic function--enabling better bidding, facilitating clearer measurement, or allowing for more relevant ad serving. Random segmentation or organization by arbitrary categories undermines the very systems designed to improve your performance.

A well-structured account creates natural boundaries that help Google's machine learning models understand your business, your customers, and the relationship between queries and outcomes. When the algorithm has clear signals about what each campaign is trying to accomplish, it can optimize more effectively--and that effectiveness translates directly to lower costs and better results.

Campaign Architecture Patterns

The most effective campaign structures follow patterns that align with business objectives rather than keyword categories. Consider organizing around outcome types--separate campaigns for leads versus sales versus website actions--rather than product lines or services. This alignment means each campaign has a clear conversion signal that bidding can optimize toward without confusion across different value actions.

For businesses with multiple service lines or product categories, hierarchical organization works best. At the campaign level, group by primary objective and geographic scope. Within campaigns, use ad groups to capture semantic keyword themes rather than exact match terms. This approach keeps data concentrated enough for learning while maintaining relevance within each grouping.

Budget segmentation deserves particular attention in advanced structures. Rather than single campaigns with shared budgets, consider separate campaigns for different performance tiers or strategic priorities. Brand campaigns often warrant isolation to prevent them from competing with non-brand campaigns for budget. High-intent commercial searches benefit from dedicated budget allocation that isn't cannibalized by informational queries. This segmentation enables more predictable spend patterns and clearer ROI measurement at the strategic level. Understanding the full picture of PPC ad pricing helps inform budget allocation decisions.

Managing Complexity at Scale

As accounts grow, complexity becomes a significant challenge. The solution isn't simplification--it's systematic organization that allows for consistent management across many campaigns and ad groups. Develop naming conventions that communicate purpose at a glance. Create template structures that can be replicated for new products or markets. Build audit processes that catch drift before it compounds.

Documentation of campaign logic becomes essential at scale. When multiple team members manage accounts or when handoffs occur, the reasoning behind structural decisions often gets lost. Document not just what exists, but why it exists--and what would need to change if business objectives shift. This documentation transforms account structure from a static arrangement into an adaptable framework that can evolve with your business.

Mastering Quality Score

The Three Pillars of Quality Score

Quality Score remains one of the most misunderstood elements of Google Ads, yet it directly influences both cost and position on every auction. Rather than treating Quality Score as a single metric, advanced practitioners understand it as an assessment of three distinct components: expected click-through rate, ad relevance, and landing page experience. Each requires specific optimization approaches. For a deep dive into how Quality Score is calculated, see our comprehensive guide on the Quality Score formula.

Expected click-through rate measures how likely your ad is to be clicked when shown for a query. This isn't about raw CTR--it's about relevance. An ad that perfectly matches user intent will have a higher expected CTR than one that's technically relevant but less compelling. Optimization here means writing ads that speak directly to the specific intent behind each keyword, using language that mirrors how searchers express their needs.

Ad relevance assesses how closely your ad matches the overall meaning of the keyword. This component is about category alignment--is this keyword about software, and does your ad clearly communicate that you offer software? Mismatches here create poor user experiences and signal to Google that your ads shouldn't serve for certain queries. Ensure every ad group has ads that speak to the core theme of its keywords, with extensions that reinforce relevance.

Landing page experience evaluates how well your landing page meets user expectations set by the ad. This goes beyond page speed (though speed matters). It encompasses relevance between ad promise and page content, transparency about what users will find, ease of navigation, and mobile experience. The landing page must deliver on the ad's implicit promise, quickly and clearly. This connects directly to our conversion rate optimization services that ensure post-click experiences match ad expectations.

Strategic Quality Score Optimization

Advanced Quality Score optimization begins before campaigns launch. The keyword-to-ad-to-landing page alignment chain must be deliberate, with each element reinforcing the others. When you add a new keyword, ask: can I write an ad that's directly relevant to this specific intent? Does my landing page address this intent immediately? If either answer is no, reconsider the keyword or develop the necessary creative assets.

Responsive Search Ads present both opportunity and challenge for Quality Score. Google's ability to mix and match headlines and descriptions means you need to provide variations that maintain relevance across different combinations. The asset report reveals which combinations serve most often--and which underperform. Use this data to eliminate low-performing assets and add variations that improve overall ad quality.

Performance Max campaigns operate differently for Quality Score since you don't directly control search ads. The quality of your asset groups--images, headlines, descriptions, logos--determines how well the algorithm can construct compelling ads across inventory. Poor assets lead to poor performance regardless of bidding. Invest in professional creative assets that communicate your value proposition clearly across formats.

Diagnosing and Addressing Low Quality Scores

When Quality Scores drop, systematic diagnosis identifies root causes. Start by examining search term reports to identify queries triggering your keywords. Low-quality queries--those semantically distant from your keywords--generate poor ad matches and drag down Quality Score. Adding these as negative keywords improves relevance for legitimate traffic.

Auction insights reveal competitive context. If competitors consistently outrank you with similar bids, Quality Score may be the differentiator. Examine their ad copy and landing pages to understand what might make their offering more relevant for shared queries. This competitive intelligence directly informs your optimization priorities.

Landing page audits should be ongoing, not reactive. Page speed degrades as sites grow. Content freshness matters. Mobile experience requires continuous attention. Establish regular review cadences that catch landing page issues before they impact Quality Score--and thus costs--across your campaigns.

Performance Max Mastery

Understanding Performance Max Dynamics

Performance Max represents Google's vision for the future of performance advertising--a fully automated campaign type that uses machine learning to optimize across all Google inventory. Understanding how PMax operates is essential for advanced practitioners, because its black-box nature requires different management approaches than traditional campaigns.

PMax works by asset combination testing. You provide headlines, descriptions, images, videos, and logos. Google's algorithm tests combinations across Search, Display, YouTube, Gmail, Discover, and Maps, learning which combinations perform best for each placement and audience. The algorithm also manages bidding and targeting, finding conversions wherever they might occur within your specified conversion actions.

This automation requires a shift in mindset. You're no longer optimizing specific ad copy for specific placements--you're providing the raw materials for the algorithm to optimize at scale. Your job becomes asset quality and strategy rather than ad copy writing. The quality and variety of assets directly limits what the algorithm can accomplish. This approach complements your broader digital marketing strategy by extending reach across Google's full inventory.

Asset Strategy for PMax Success

Image assets are the visual foundation of PMax performance. Google recommends 5-15 images at various aspect ratios, including square, landscape, and portrait formats. But beyond quantity, focus on variety that covers different value propositions, product angles, and messaging tones. Include lifestyle images that show your offering in use, product-focused shots that highlight key features, and promotional graphics that communicate offers or differentiators.

Video assets have become increasingly important as Google expands video inventory. Even simple slideshow videos created from static images can improve campaign performance compared to campaigns without video. If video production resources are limited, prioritize short-form video (15-30 seconds) that communicates core value quickly. The algorithm uses video across YouTube and in-display contexts, so variety in length and messaging helps find optimal combinations.

Headlines and descriptions should cover multiple aspects of your offering. Include specific value propositions, call-to-action variations, and differentiators that might resonate with various audience segments. The algorithm tests combinations to find what works--but it can only work with what you provide. Five headlines that all say essentially the same thing provide less testing opportunity than five that communicate distinct ideas.

Guardrails and Strategic Controls

The criticism of PMax often centers on loss of control--and there is truth to this concern. However, Google has added controls that allow practitioners to guide the algorithm without micromanaging it. Understanding and implementing these controls is essential for advanced PMax management.

Audience signals allow you to indicate which audiences are most likely to convert. These aren't restrictions--the algorithm can still show ads to anyone--but they provide learning signals that help prioritize. Use customer match lists, in-market audiences, and affinity audiences to indicate your best prospects. As the algorithm learns, it will weight these signals appropriately.

Brand restrictions prevent PMax from showing your ads alongside competitor content or from triggering on competitor brand terms unless you explicitly want this behavior. Configure these settings based on your strategic priorities. For some businesses, competitor targeting is essential; for others, it's wasteful.

Geographic and budget controls remain in your hands. PMax respects campaign-level geographic settings and budget allocations. Ensure these align with your business objectives and that PMax campaigns aren't accidentally competing with more controlled campaign types for budget.

Measuring and Optimizing PMax

PMax attribution requires accepting that incrementality is the right framework, not last-click attribution. PMax generates conversions that might never have happened through other channels--and you need to understand this incremental value. Use holdout testing where possible, or examine cross-channel patterns that suggest PMax is capturing attention that Search converts.

The asset performance report reveals which specific assets drive results. Underperforming assets should be replaced, not deleted--replacement allows the algorithm to learn without losing historical data. High-performing assets should inspire variations that test similar themes with different executions.

Search term insights for PMax show what searches triggered your ads, enabling negative keyword refinement that improves relevance without restricting reach. This is one area where you can directly influence PMax targeting, so use it strategically to eliminate irrelevant triggers while preserving valuable queries.

Advanced Bidding Strategies

Matching Bidding to Business Objectives

Google's automated bidding options have matured significantly, and selecting the right strategy requires understanding both the options available and your specific business objectives. No single bidding strategy works for every situation--the right choice depends on your conversion volume, data availability, and strategic priorities.

Maximize Conversions works best when you have sufficient conversion volume (ideally 50+ conversions per month per campaign) and want the algorithm to find additional conversion opportunities. This strategy trades some efficiency for volume, accepting that it may pay more per conversion to generate more total conversions. Best for growth phases where top-line metrics matter more than unit economics.

Target CPA (cost per acquisition) is appropriate when you have stable conversion data and want predictable unit economics. The algorithm attempts to get conversions at your target cost, potentially limiting volume if that target is aggressive. Works best with historical data that establishes reliable conversion patterns. Requires monitoring to ensure targets are achievable without artificially limiting reach.

Target ROAS (return on ad spend) prioritizes revenue efficiency over conversion volume. Essential for e-commerce businesses where different products have different margins. Requires accurate conversion values and sufficient transaction volume for the algorithm to learn patterns. Can underperform in accounts with high average order values and low conversion volumes.

Maximize Clicks serves when visibility and traffic matter more than immediate conversions--for consideration-stage campaigns or when building audience lists. Not a performance strategy but a tactical choice for specific objectives.

Hybrid Approaches for Complex Accounts

Many accounts benefit from layering strategies rather than relying on a single bidding approach. Consider separating campaigns by conversion stage: upper-funnel awareness campaigns using Maximize Clicks or Target Impression Share, mid-funnel consideration campaigns using Target CPA, and lower-funnel campaigns using Target ROAS. This segmentation allows each campaign to optimize for its appropriate metric.

Portfolio bidding allows applying a single bidding strategy across multiple campaigns with a shared performance target. This is valuable when individual campaigns have sparse conversion data but aggregate data is sufficient for algorithmic learning. Portfolio approaches work for geo-targeted campaigns, product-line campaigns, or any scenario where consolidation improves data availability.

Seasonal Adjustments and Testing Methodologies

Seasonal adjustments require active management regardless of bidding strategy. During predictable high-volume periods--holiday shopping seasons, industry events, or promotional windows--consider temporarily raising CPA targets or switching to Maximize Conversions to capture increased intent. Post-period, return to efficiency-focused bidding. Build these adjustments into your annual calendar and implement them before shifts occur. The key is anticipation rather than reaction; prepare bidding strategies before seasonal demand spikes rather than scrambling to adjust when competition intensifies.

Bidding strategy testing requires disciplined methodology to generate reliable insights. Run parallel campaigns with identical keywords and ad copy but different bidding strategies--Maximize Conversions versus Target CPA, for example--to isolate the impact of bidding alone. Tests should run for a minimum of four weeks to account for weekly patterns and ensure algorithmic learning has stabilized. Document both the primary metric (conversions, CPA, or ROAS) and secondary metrics (impression share, click volume, and conversion rate) to understand full implications.

Before launching bidding tests, establish clear hypotheses: what do you expect to change, and why? Document success criteria before seeing results to prevent post-hoc rationalization. After tests complete, analyze not just which strategy performed better, but why--understanding the underlying mechanics enables better future decisions. This testing discipline compounds over time, building institutional knowledge about how bidding strategies perform under different conditions.

Data Requirements for Smart Bidding

Smart bidding's effectiveness depends on data quality and quantity. Understanding these requirements helps set realistic expectations and identify when additional signals might improve performance.

Conversion tracking must be accurate and comprehensive. Misattributed conversions train algorithms poorly, creating optimization toward the wrong behaviors. Audit conversion tracking regularly, comparing Google Ads conversion data to backend records. Address discrepancies before they compound into optimization errors.

Conversion windows and view-through conversions significantly impact algorithmic learning. Longer windows capture more attribution credit but may include conversions influenced by multiple touchpoints. Test different window settings to find what aligns with your actual customer journey. View-through conversions provide learning signals for awareness campaigns but can inflate apparent performance for consideration campaigns.

Audience signals provide additional context that improves bidding decisions. Customer match lists, similar audiences, and in-market segments all contribute signals about conversion probability. Even when not directly targeting these audiences, including them as signals helps the algorithm understand which users are most valuable.

Testing Frameworks That Scale

The Discipline of Systematic Testing

High-performing accounts don't achieve their results through single optimizations--they compound improvements through continuous testing. This requires moving from ad-hoc testing to systematic frameworks that generate reliable insights over time. Our guide on developing PPC testing strategies provides a deeper dive into building scalable testing programs.

Effective testing frameworks have several characteristics. They start with hypotheses grounded in data, not guesses about what might work. They define success criteria before launching tests, preventing post-hoc rationalization of results. They run for sufficient duration to reach statistical significance, avoiding premature conclusions based on limited data. And they document findings in ways that inform future test design.

The testing mindset requires accepting that most tests will fail--or rather, that tests reveal what doesn't work as much as what does. This isn't failure; it's progress. Each test adds to your understanding of your audience, your messaging, and your competitive position. The compound effect of many tests is performance that outpaces competitors who rely on intuition rather than evidence.

Structural Tests

Structural tests examine how account organization affects performance. These tests take longer to run but often yield larger improvements than copy tests.

Campaign structure tests compare different approaches to segmentation. Does organizing by product category outperform organizing by funnel stage? Does geographic segmentation improve efficiency compared to single campaigns with location bid adjustments? These tests require significant time and budget but reveal structural optimizations that affect everything downstream.

Match type experiments examine the balance between reach and relevance across different match type configurations. Traditional wisdom held that exact match provided control while broad match provided reach--but Google's match type behavior has evolved. Tests comparing exact-only to phrase-plus-broad, or examining modified broad match behavior, reveal what works for your specific account and keywords.

Bidding strategy comparisons require even longer test periods but provide essential insights. Running parallel campaigns with different bidding strategies (Maximize Conversions versus Target CPA, for example) reveals which approach actually delivers better results for your business, rather than relying on Google's recommendations or industry conventional wisdom.

Creative Tests

Creative tests examine messaging, offers, and ad composition. These tests run faster but require consistent attention to generate compounding improvements.

Headline tests examine different value proposition framings. Test specificity versus generality, benefit-focused versus feature-focused, urgency-driven versus confidence-building. The asset report provides data, but test design must isolate variables to generate actionable insights. A well-designed test changes one element at a time--testing five headline changes simultaneously tells you something works but not what.

Extension tests examine which extension types and messages drive the best performance. Sitelink copy, callout text, and structured snippet categories all provide opportunities to differentiate and provide additional value. Systematic rotation and testing of extension copy reveals what resonates beyond the core ad. A single improvement to sitelink copy can lift click-through rate across an entire campaign.

Landing page tests extend beyond Google Ads into the conversion experience. Even perfect ads can't compensate for poor landing pages. Test landing page variations that maintain ad copy promises while improving the post-click experience. These tests require coordination with web teams but directly impact true ROI. Coordinate these tests with your web development team to ensure technical feasibility and proper implementation.

Case Study Patterns from Successful Testing Programs

Analysis across testing programs reveals consistent patterns. Tests that isolate single variables generate more actionable insights than multivariate tests, even though multivariate testing seems more efficient. The reason is simple: when multiple elements change simultaneously, you know something improved but cannot identify what.

Duration requirements vary by test type. Creative tests typically need 1-2 weeks to reach statistical significance, assuming sufficient traffic. Structural tests require 4-8 weeks to account for learning periods and seasonal variation. Bidding strategy tests fall somewhere in between--long enough for algorithms to learn, but not so long that opportunity cost becomes significant.

Documentation matters enormously. Teams that document hypotheses, success criteria, and findings generate compounding value from testing. Teams that run tests without documentation repeat failures and miss opportunities to build on successes. Create a testing repository that captures what was tested, why, and what was learned. This repository becomes a strategic asset that improves decision-making across the account.

Audience Strategy and Targeting

Building Sophisticated Audience Layers

Audience targeting in Google Ads has evolved from simple demographic categories to sophisticated layering that combines intent, behavior, and custom segments. Advanced practitioners build audience strategies that reach the right people with the right messages at the right moments.

Customer match forms the foundation of sophisticated audience strategies. Upload first-party customer data to create lists of existing customers, then use these lists for exclusion (preventing wasted spend on current customers) or for targeting (reaching customers for repeat purchases or different products). The accuracy and recency of your customer data directly affects list quality. For B2B companies, this means uploading CRM data to reach decision-makers; for e-commerce, it means excluding past purchasers from prospecting campaigns.

Similar audience expansion takes seed lists--whether from customer match or website visitors--and finds users who share characteristics. This expansion provides scale while maintaining affinity with your best customers. Test different seed lists to understand which produce most valuable similar audiences for your business. Customer-based similar audiences typically outperform visitor-based similar audiences because they represent known converters rather than browsers.

In-market audiences indicate active purchase consideration. These audiences are valuable for consideration-stage messaging but can be overly broad for high-consideration purchases. Test in-market targeting against contextual targeting to understand whether explicit intent signals outperform implicit behavioral signals for your specific offering.

Remarketing Segmentation and Exclusion Strategies

Remarketing is where sophisticated audience strategy delivers the clearest results--but only when implemented thoughtfully. Basic remarketing (showing ads to all past visitors) is better than no remarketing, but advanced strategies achieve significantly better efficiency.

Segmentation-based remarketing separates past visitors by recency, behavior, and depth of engagement. Someone who visited once and left after five seconds deserves different messaging than someone who viewed pricing, read testimonials, and added an item to cart. Build remarketing lists that capture these behavioral differences, then craft messaging that acknowledges where each visitor is in their journey.

Consider these segmentation examples:

• Product-page visitors: Show dynamic remarketing with specific products viewed, combined with social proof and urgency messaging
• Cart abandoners: Emphasize completion benefits, offer assistance, and remind of items left behind
• Pricing page visitors: Provide additional information, compare features, and offer consultations or demos
• Email subscribers who haven't converted: Test different creative angles since they already know your brand

Audience exclusion strategies prevent waste across all campaign types. Exclude current customers from prospecting campaigns unless upsell is the objective. Exclude low-intent audiences from high-intent campaigns to preserve budget for likely converters. Exclude converted users for a defined window (30-90 days) to prevent spending on users who have already taken action.

The key principle is relevant exclusion. Every exclusion should have a strategic rationale--wasting budget on users who cannot or will not convert. Document your exclusion logic and review regularly as business objectives evolve.

Audience for Performance Max

Performance Max uses audience signals differently than traditional campaigns, but the principle of providing strong signals remains essential. Feed PMax the same audience intelligence you'd use for targeting elsewhere.

Customer match lists as signals help PMax find users similar to your best customers. Even if you're not restricting PMax to these audiences, including them as signals gives the algorithm guidance about who to prioritize.

In-market and affinity audiences provide intent signals that help PMax understand purchase-ready users. Include relevant audiences even if you don't plan to use them for targeting elsewhere--the signals improve algorithmic learning.

Website visitor segments (created via the Google Ads tag) provide behavioral signals about user interests and intent. These segments help PMax understand what your best prospects have in common, enabling similar user finding and improved relevance. For best results, upload high-value segments--users who converted, users with high engagement time, users who viewed key conversion pages.

Data-Driven Optimization

Building an Optimization Cadence

Optimization isn't a one-time activity--it's a continuous process that compounds improvements over time. The most successful practitioners build optimization rhythms that ensure consistent attention to performance without getting lost in daily tactical adjustments.

Daily optimization focuses on alerts and anomalies. Review any campaigns showing significant performance shifts, address budget delivery issues, and check for disapproved ads or policy issues. Most campaigns won't need daily attention, but daily review ensures issues are caught quickly before they waste significant budget.

Weekly optimization examines performance patterns and makes tactical adjustments. Review search term reports to add negative keywords and discover new opportunities. Examine underperforming keywords for pause or restructure. Check that campaigns are delivering as expected and adjust bids or budgets based on the week's results. This connects to our analytics and reporting services that help track and interpret these patterns systematically.

Monthly optimization takes the strategic view. Analyze performance trends across longer timeframes. Identify structural opportunities that weekly reviews miss. Review Quality Score patterns and landing page performance. Assess whether campaign structure still aligns with business objectives. Compare month-over-month and year-over-year to understand true performance trajectory.

Quarterly optimization addresses the big picture. Evaluate whether bidding strategies are still appropriate. Assess whether testing programs are generating useful insights. Review competitive landscape for significant changes. Plan major structural changes or new initiatives. This strategic view ensures that tactical optimization supports rather than conflicts with business objectives.

Metrics That Matter

Effective optimization requires clear understanding of which metrics actually indicate performance--and which can mislead.

Conversion rate tells you how well your ads and landing pages turn clicks into desired actions. But context matters: a high conversion rate with low volume may indicate narrow targeting, while a low conversion rate with high volume may indicate broad reach. Always consider conversion rate alongside conversion volume and CPA.

Cost per conversion is the efficiency metric that ultimately matters for performance campaigns. But don't optimize CPA in isolation--a lower CPA with significantly lower volume may not be progress. Consider both metrics together, and understand what trade-offs are acceptable for your business.

Impression share reveals how often you're winning available auctions. Low impression share due to low rank indicates bid or quality issues. Low impression share due to budget indicates opportunity to scale. Understanding the cause determines the appropriate response.

Search impression lost to budget and search impression lost to rank disaggregate the reasons for missed impressions. If lost to rank dominates, focus on bid or Quality Score improvements. If lost to budget dominates, consider budget increases or reallocation.

Interpreting Data Correctly

Data analysis requires avoiding common traps that lead to incorrect conclusions.

Correlation isn't causation--just because two metrics move together doesn't mean one causes the other. Before implementing changes based on observed correlations, test hypotheses explicitly to confirm causal relationships.

Survivorship bias leads to over-optimizing based on successful elements while ignoring failures. Account for the full picture, not just what's working. Sometimes the best optimization is eliminating what's not working rather than amplifying what is.

Regression to the mean means that unusually good (or bad) performance often represents random variation rather than genuine change. Before declaring a winning strategy, ensure performance is consistent enough to suggest real improvement rather than statistical noise. Run tests multiple times or extend duration before making permanent changes based on exceptional results.

Sources

1. Define Digital Academy: Google Ads Optimization Tips and Best Practices 2025 - Search term review cadence, campaign structure principles, ad testing methodology, optimization scheduling framework

2. CXL: PPC in 2025 Control the Algorithm - AI algorithm control strategies, data foundations for automated bidding, guardrails for performance max

3. RedTrack: Google Ads Best Practices 2025 - Campaign setup optimization, budget management strategies

4. Interteam Marketing: B2B Google Ads Guide 2025 - Account structure, targeting strategies, keyword research approaches