Resource Allocation Planning

Resource Allocation Planning in Traditional SEO versus Generative Engine Optimization (GEO) represents the strategic distribution of budget, personnel, time, and technological assets between conventional search engine optimization practices and emerging optimization strategies for AI-powered generative engines like ChatGPT, Google's Search Generative Experience (SGE), and Bing Chat ¹². This planning discipline serves the primary purpose of maximizing organizational visibility and traffic acquisition across both traditional search results and AI-generated responses while managing finite resources effectively. The significance of this planning has intensified dramatically as generative AI engines have begun reshaping how users discover information, with studies indicating that AI overviews and chatbot responses are increasingly intercepting traditional search traffic ²⁶. Organizations must now strategically balance investments between proven SEO methodologies and experimental GEO tactics to maintain competitive advantage in an evolving digital landscape.

Overview

The emergence of Resource Allocation Planning between Traditional SEO and GEO stems from a fundamental shift in how users access information online. For over two decades, traditional search engine optimization focused exclusively on ranking web pages in conventional search engine results pages (SERPs) through techniques including keyword optimization, link building, and technical site improvements ³. However, the introduction of generative AI systems capable of synthesizing information and providing direct answers has created a parallel channel for information discovery that operates under different principles ¹².

The fundamental challenge this planning discipline addresses is the strategic dilemma organizations face when confronting finite resources and two distinct optimization paradigms. While traditional SEO remains essential for direct website traffic and has well-established measurement frameworks, GEO represents an emerging channel that may dominate information discovery as AI adoption accelerates but carries significant uncertainty regarding ROI and best practices ⁵⁶. Research from industry analysts suggests that generative engines are already handling billions of queries monthly, creating urgency for strategic resource reallocation ².

The practice has evolved rapidly since 2023, when generative AI search experiences began mainstream deployment. Initially, organizations adopted a "wait-and-see" approach, maintaining 95-100% allocation to traditional SEO ⁶. As evidence accumulated regarding AI's impact on search behavior—particularly for informational queries—forward-thinking companies began shifting 10-20% of resources toward GEO experimentation ¹⁵. This evolution continues as measurement capabilities mature and competitive pressures intensify, with some organizations in information-intensive industries now allocating 30-40% of search budgets to GEO initiatives ¹.

Key Concepts

Zero-Click Searches

Zero-click searches refer to queries answered directly in search engine results pages or AI-generated responses without requiring users to click through to a website ³. This phenomenon represents a critical consideration in resource allocation, as content optimized for visibility may not generate direct traffic. According to industry data, approximately 25% of searches result in zero clicks, with this percentage increasing for informational queries that generative engines handle particularly well ³.

Example: A healthcare organization publishing comprehensive articles about diabetes management might achieve high traditional SEO rankings and frequent citations in AI-generated health responses. However, users obtaining complete answers from AI summaries or featured snippets never visit the organization's website, eliminating opportunities for newsletter signups, appointment bookings, or other conversion actions. This reality forces the organization to balance visibility optimization (which builds brand authority) against traffic generation (which drives direct conversions), influencing whether to allocate resources toward GEO-optimized informational content or traditional SEO-focused commercial content.

AI Attribution and Citation

AI attribution occurs when generative engines cite, reference, or acknowledge specific sources while synthesizing information in their responses ¹⁵. Unlike traditional search rankings where position determines visibility, GEO success depends on whether AI systems select content as authoritative and citation-worthy during their synthesis process.

Example: A financial services firm publishing detailed analysis of Federal Reserve policy decisions might invest heavily in expert credentials, rigorous fact-checking, and structured data implementation to increase citation probability in AI-generated financial news summaries. When ChatGPT or Google's SGE generates responses to queries like "How will interest rate changes affect mortgage rates?", the firm's content appears as a cited source with attribution. This citation builds brand authority among users who may not have discovered the firm through traditional search, justifying resource allocation toward GEO-optimized thought leadership content despite uncertain direct traffic impact.

Portfolio Optimization Framework

The Portfolio Optimization Framework adapts modern portfolio theory from finance, treating SEO and GEO as distinct asset classes with different risk-return profiles ¹. Organizations calculate expected returns (traffic, conversions, revenue) and volatility (uncertainty, algorithm changes) for each channel, then optimize allocation to maximize return for acceptable risk levels.

Example: An enterprise SaaS company serving IT professionals analyzes its target audience's high AI adoption rate (65% regularly use ChatGPT for technical research) and maps its content portfolio across query types. Informational queries about "cloud security best practices" show 40% AI interception, while commercial queries like "enterprise firewall pricing" maintain 90% traditional search dominance. Using portfolio optimization, the company allocates 70% of its $500,000 annual search budget to traditional SEO (focusing on commercial content, product pages, and comparison guides) and 30% to GEO (focusing on technical documentation, thought leadership, and educational content). This allocation balances proven ROI from traditional SEO against strategic positioning in emerging AI channels where their technical audience increasingly discovers information.

Structured Data Implementation

Structured data implementation involves adding Schema.org markup and semantic HTML to content, enabling both traditional search engines and AI systems to better understand, categorize, and utilize information ⁴. This represents a critical intersection where GEO investments strengthen traditional SEO outcomes through enhanced rich results and featured snippets.

Example: An e-commerce retailer selling outdoor equipment implements comprehensive structured data across product pages, including detailed specifications, customer reviews, pricing, and availability information using Schema.org Product markup ⁴. This investment, initially justified for traditional SEO benefits (rich snippets in Google search results), also positions products for citation in AI-generated shopping recommendations. When users ask generative engines "What's the best backpacking tent under $300?", the retailer's products appear in synthesized recommendations with accurate specifications pulled from structured data. The dual benefit—improved traditional search visibility and increased GEO citation probability—justifies allocating 15% of technical SEO resources to advanced structured data implementation beyond basic requirements.

Query Intent Mapping

Query Intent Mapping involves categorizing target queries by intent type (informational, navigational, transactional) and analyzing how each category performs across traditional search versus generative AI channels ¹³. This methodology enables data-driven resource allocation aligned with channel-specific query performance.

Example: A legal services firm conducts comprehensive query intent analysis across 500 target keywords. They discover that informational queries ("What is a living trust?", "How does bankruptcy work?") show 55% AI interception rates, with users receiving complete answers from generative engines. Navigational queries ("Smith & Associates law firm") maintain 95% traditional search dominance. Transactional queries ("hire estate planning attorney Boston") show 80% traditional search preference, as users want direct contact with service providers. Based on this mapping, the firm allocates 60% of content resources to traditional SEO-optimized service pages and local listings, 25% to GEO-optimized educational content that builds authority even without direct traffic, and 15% to hybrid content serving both channels.

Incremental Testing Framework

The Incremental Testing Framework employs controlled experimentation, allocating small resource percentages (5-10%) to GEO pilots, measuring results against control groups, then scaling successful initiatives ¹. This approach minimizes risk while building empirical evidence for larger allocation shifts.

Example: A B2B software publisher with 12 content writers dedicates one writer (8% of content resources) exclusively to GEO-optimized production for six months. This writer creates highly authoritative, citation-worthy articles with extensive expert input, rigorous sourcing, and advanced structured data—production standards requiring 40% more time per piece than traditional SEO content. The publisher tracks AI citation frequency, brand mentions in generative responses, and any attributable traffic using UTM parameters and brand monitoring tools. After six months, data shows the GEO-optimized content receives 3x more AI citations and generates measurable brand awareness lift, though direct traffic remains 60% lower than traditional SEO content. Based on these results, the publisher expands GEO allocation to three writers (25% of resources), validating the approach before committing majority resources.

Visibility Share Metrics

Visibility share represents the proportion of target audience reached through each channel—traditional search results versus AI-generated responses ¹². Unlike traditional metrics focused solely on website traffic, visibility share acknowledges that brand exposure in AI citations delivers value even without clicks.

Example: A cybersecurity firm tracks visibility across both channels for 200 target queries. Traditional SEO monitoring shows they rank in top 10 positions for 65% of queries, generating 50,000 monthly organic visits. Separately, they monitor AI citations using brand tracking tools, discovering their content appears in ChatGPT responses for 40% of queries and Google SGE results for 35% of queries, with estimated monthly exposure to 75,000 users who receive AI-generated answers. Total visibility share (combined traditional and AI exposure) reaches 125,000 monthly users, though only 40% generate website visits. This comprehensive visibility measurement justifies continued GEO investment despite lower direct traffic, as the firm values brand authority building and top-of-funnel awareness that may drive conversions through other channels.

Applications in Digital Marketing Strategy

Enterprise SaaS Content Strategy

Enterprise software companies apply resource allocation planning by implementing 70/30 traditional SEO/GEO splits, focusing GEO efforts on thought leadership content and technical documentation that AI systems frequently cite ¹. These organizations recognize that technical buyers increasingly use AI assistants for research, making citation in AI-generated responses critical for consideration set inclusion. Traditional SEO resources focus on product comparison pages, pricing information, and commercial content where users prefer direct website access. This allocation acknowledges different user behaviors across the buyer journey—AI-assisted research for early-stage education, traditional search for late-stage evaluation and purchase.

E-Commerce Product Discovery

E-commerce retailers maintain 90/10 traditional SEO/GEO allocation splits, recognizing that product discovery remains predominantly traditional search-driven ¹³. Users seeking to purchase specific products prefer browsing actual product pages with images, detailed specifications, customer reviews, and purchasing options rather than AI-generated summaries. However, the 10% GEO allocation focuses on structured data implementation that serves dual purposes—enhancing traditional search rich results while positioning products for citation in AI shopping recommendations. Retailers also allocate GEO resources toward educational buying guides and product comparison content where AI synthesis adds value for research-phase users.

Media and Publishing Information Distribution

Media publishers experiment with 50/50 allocation approaches, as their informational content aligns well with AI synthesis while maintaining traditional search traffic for breaking news and entertainment content ¹⁶. Publishers recognize that generative engines excel at synthesizing factual, educational content—exactly the material publishers produce. However, breaking news, local coverage, and entertainment content maintain strong traditional search performance as users seek current information and diverse perspectives. Publishers allocate GEO resources toward evergreen educational content, expert analysis, and data-driven journalism that AI systems cite as authoritative sources, while maintaining traditional SEO focus for time-sensitive and entertainment content.

Local Service Business Optimization

Local service businesses (restaurants, contractors, healthcare providers) maintain 95/5 traditional SEO/GEO allocations, prioritizing local search optimization, Google Business Profile management, and review generation ³. These businesses recognize that local intent queries ("plumber near me", "best Italian restaurant downtown") overwhelmingly drive traditional search results with map packs and local listings. The minimal 5% GEO allocation focuses on FAQ content and educational resources that may appear in AI-generated local recommendations, but core resources remain dedicated to proven traditional local SEO tactics that directly drive phone calls and foot traffic.

Best Practices

Implement Integrated Measurement Frameworks

Organizations should establish comprehensive measurement systems tracking both traditional SEO metrics (rankings, organic traffic, conversions) and GEO-specific indicators (AI citation frequency, visibility in generative responses, brand mention rates) ¹². The rationale recognizes that optimizing for one channel while ignoring the other creates blind spots in understanding total search visibility and competitive positioning.

Implementation Example: A financial services firm implements a dual-tracking dashboard combining Google Search Console data (traditional rankings, clicks, impressions) with AI monitoring tools tracking brand mentions across ChatGPT, Google SGE, and Bing Chat. They establish weekly reporting showing traditional search traffic trends alongside AI citation frequency, enabling data-driven reallocation decisions. When they observe 15% quarterly decline in traditional search traffic for informational queries but 40% increase in AI citations for the same topics, they reallocate resources from traditional informational content toward commercial content while maintaining GEO investment in thought leadership that drives AI visibility.

Prioritize Structured Data as Dual-Benefit Investment

Organizations should prioritize structured data implementation using Schema.org markup as a foundational investment benefiting both traditional SEO and GEO simultaneously ¹⁴. The rationale recognizes that structured data enhances traditional search rich results while improving AI comprehension and citation probability, delivering compounding returns across both channels.

Implementation Example: An e-commerce retailer allocates $75,000 to implement comprehensive structured data across 50,000 product pages, including Product, Review, Offer, and Organization schemas ⁴. This investment generates immediate traditional SEO benefits through enhanced rich snippets showing star ratings, pricing, and availability in search results, increasing click-through rates by 23%. Simultaneously, the structured data improves product citation in AI-generated shopping recommendations, as generative engines can accurately extract specifications, pricing, and availability. The dual benefit justifies the investment despite uncertain GEO ROI, as traditional SEO returns alone exceed cost while GEO benefits provide additional upside.

Adopt Audience-Segmented Allocation Strategies

Organizations should segment target audiences by AI adoption patterns and allocate resources proportionally to each segment's channel preference rather than applying uniform allocation across all content ¹⁶. The rationale acknowledges that different demographics and use cases show vastly different AI adoption rates, making one-size-fits-all allocation suboptimal.

Implementation Example: A healthcare organization segments its audience into three groups: medical professionals (75% AI adoption for clinical research), patients seeking general health information (35% AI adoption), and individuals seeking appointment booking (10% AI adoption). They allocate 40% of resources to GEO-optimized clinical content targeting medical professionals, 30% to hybrid content serving both channels for general health information, and 30% to traditional SEO-optimized appointment booking and service pages. This segmented approach delivers superior results compared to uniform 33/33/33 allocation, as resources align with actual channel usage patterns across audience segments.

Establish Quarterly Rebalancing Triggers

Organizations should implement systematic quarterly reviews with predefined triggers for resource reallocation rather than static annual planning ¹. The rationale recognizes the rapid evolution of generative AI capabilities and search behavior, requiring dynamic allocation adjustments as empirical evidence accumulates.

Implementation Example: A B2B technology company establishes quarterly allocation reviews with specific rebalancing triggers: if AI citation frequency increases by more than 25% quarter-over-quarter, increase GEO allocation by 5 percentage points; if traditional SEO traffic declines by more than 15% for informational queries, shift resources from informational to commercial content; if GEO-attributed conversions (tracked through brand search lift) exceed 10% of total conversions, increase GEO budget by 10%. These predefined triggers enable rapid response to changing dynamics while avoiding reactive decision-making based on anecdotal evidence or short-term fluctuations.

Implementation Considerations

Tool Selection and Analytics Infrastructure

Organizations must carefully evaluate tool choices for tracking performance across both traditional SEO and GEO channels ¹². Traditional SEO benefits from mature analytics platforms like Google Search Console, SEMrush, and Ahrefs with established metrics and reporting ³. However, GEO requires emerging solutions for monitoring AI citations, tracking brand mentions in generative responses, and analyzing content appearance in AI outputs. Best practices include starting with free or low-cost monitoring solutions to establish baseline GEO metrics before investing in premium tools, prioritizing platforms offering integrated traditional SEO and GEO capabilities, and maintaining flexibility to switch vendors as the market matures. Organizations should avoid over-investing in proprietary GEO tools before industry standards emerge, as the measurement landscape remains fluid with new solutions launching frequently.

Example: A mid-sized publisher initially uses free brand monitoring tools and manual ChatGPT queries to track AI citations, establishing baseline metrics without significant investment. After six months demonstrating measurable GEO impact, they invest in an integrated platform combining traditional SEO analytics with AI citation tracking, justifying the $2,000 monthly cost through demonstrated value rather than speculative benefits.

Audience-Specific Customization

Resource allocation should reflect target audience AI adoption patterns, which vary significantly by demographics, industry, and use case ¹⁶. B2B technology audiences show substantially higher AI adoption rates (60-70%) compared to general consumer audiences (20-30%), influencing optimal allocation ratios. Organizations serving multiple audience segments should implement differentiated allocation strategies rather than uniform approaches.

Example: A professional services firm serving both technology companies and traditional manufacturing clients implements segmented allocation: 40% GEO/60% traditional SEO for technology-focused content targeting CIOs and IT directors who heavily use AI research tools, versus 10% GEO/90% traditional SEO for manufacturing-focused content targeting operations managers with lower AI adoption. This customization delivers superior results compared to uniform 25/75 allocation across all content.

Organizational Maturity and Change Management

Implementation success depends heavily on organizational readiness, including team capabilities, stakeholder buy-in, and change management processes ¹. Organizations with mature SEO programs and data-driven cultures typically navigate allocation shifts more successfully than those with limited analytics capabilities or resistance to experimentation. Best practices include securing executive sponsorship before significant reallocation, implementing pilot programs to demonstrate GEO value and build internal expertise, and investing in training to upskill existing teams rather than creating siloed GEO specialists disconnected from SEO operations.

Example: An enterprise retailer facing team resistance to GEO investment implements a three-month pilot where one senior SEO specialist dedicates 50% time to GEO experimentation while maintaining traditional responsibilities. The pilot demonstrates measurable AI citation increases and brand awareness lift, converting skeptical team members into GEO advocates. Following pilot success, the organization expands GEO allocation with broad team support rather than forcing change through top-down mandates that generate resistance.

Budget Constraints and Prioritization

Smaller organizations with limited budgets face difficult trade-offs between traditional SEO and GEO investment ¹³. Best practices include focusing GEO efforts on highest-authority content pieces that justify additional investment rather than attempting comprehensive GEO optimization across all content, leveraging existing content for GEO enhancement rather than creating entirely new assets, and prioritizing structured data implementation that delivers dual benefits across both channels. Organizations should avoid the pitfall of abandoning proven traditional SEO tactics prematurely based on GEO hype, instead implementing incremental shifts that preserve core traffic sources while building GEO capabilities.

Example: A small B2B software company with a $50,000 annual search budget allocates $42,500 (85%) to traditional SEO maintaining proven traffic sources and $7,500 (15%) to GEO experimentation focused exclusively on their 10 highest-authority thought leadership articles. This focused approach delivers measurable GEO results without jeopardizing core traffic, whereas attempting comprehensive GEO optimization across 200+ articles would dilute resources and undermine both channels.

Common Challenges and Solutions

Challenge: Measurement and Attribution Difficulty

Organizations struggle to quantify GEO performance due to limited analytics infrastructure for tracking AI citations, attributing traffic or conversions to generative engine visibility, and calculating accurate ROI ¹². Unlike traditional SEO where Google Search Console provides comprehensive data on rankings, impressions, and clicks, GEO lacks standardized measurement tools. This measurement gap creates executive skepticism about GEO investment and difficulty demonstrating value, potentially leading to premature abandonment of promising initiatives or continued investment in ineffective tactics without performance feedback.

Solution:

Implement multi-layered measurement approaches combining available tools and proxy metrics that correlate with GEO success ¹². Establish brand monitoring across AI platforms using tools that track mentions in ChatGPT, Google SGE, and Bing Chat responses, even if direct attribution remains imperfect. Track branded search volume increases as a proxy for AI-driven awareness, recognizing that users discovering brands through AI citations often conduct subsequent branded searches. Monitor referral traffic patterns from AI platforms using UTM parameters and referral source analysis. Establish leading indicators like structured data coverage, expert credential development, and citation-worthy content production that predict future GEO success even before lagging traffic metrics materialize. Create control groups comparing performance of GEO-optimized content against traditional SEO content to isolate impact. Set realistic expectations with stakeholders that GEO measurement remains imperfect but improving, focusing on directional trends rather than precise attribution.

Challenge: Organizational Resistance and Skill Gaps

Teams comfortable with traditional SEO often resist resource reallocation toward unproven GEO tactics, creating implementation friction ¹. Additionally, GEO requires different skill sets—understanding how LLMs process information, advanced structured data expertise, content formatting for AI comprehension—that existing SEO teams may lack. This resistance and capability gap can manifest as passive resistance (slow implementation, minimal effort), active opposition (challenging GEO value), or simple inability to execute effectively despite willingness.

Solution:

Secure executive sponsorship before significant reallocation, ensuring leadership communicates strategic importance and provides air cover for experimentation ¹. Implement pilot programs demonstrating GEO value with small resource commitments, converting skeptics through empirical results rather than theoretical arguments. Invest in comprehensive training programs upskilling existing teams in GEO principles, LLM functionality, and AI-optimized content creation rather than hiring external specialists who may lack organizational context and create team division. Frame GEO as evolution of existing SEO expertise rather than replacement, emphasizing complementary nature and skill transferability. Celebrate early wins publicly, recognizing team members who successfully implement GEO tactics and achieve measurable results. Create integrated teams with shared objectives spanning both traditional SEO and GEO rather than siloed groups competing for resources, fostering collaboration instead of competition.

Challenge: Balancing Short-Term Performance with Long-Term Positioning

Organizations face tension between maintaining proven traditional SEO performance delivering immediate traffic and conversions versus investing in GEO for long-term positioning as search behavior evolves ¹⁶. Executives focused on quarterly results may resist GEO allocation that reduces short-term traditional SEO investment, while over-indexing on GEO before mainstream adoption risks sacrificing current revenue for uncertain future benefits. This temporal trade-off creates strategic dilemmas without clear resolution.

Solution:

Implement portfolio approaches explicitly balancing short-term and long-term objectives with defined allocation ranges ¹. Establish minimum traditional SEO allocation (typically 60-70%) protecting core traffic sources and revenue generation while dedicating defined percentages (15-30%) to GEO positioning for future search landscapes. Use scenario planning to model different adoption curves—rapid AI dominance, gradual transition, sustained coexistence—and stress-test allocation strategies against each scenario, selecting approaches robust across multiple futures rather than optimizing for single predictions. Communicate explicitly with executives about the strategic trade-off, framing GEO investment as insurance against search disruption rather than guaranteed returns. Establish leading indicators demonstrating GEO progress (citation frequency, structured data coverage, authority building) that provide confidence in long-term positioning even before traffic materialization. Implement quarterly rebalancing enabling dynamic adjustment as adoption patterns clarify, avoiding locked-in commitments that prove suboptimal as evidence accumulates.

Challenge: Content Production Cost Increases

Content optimized for AI citation typically requires more rigorous fact-checking, expert input, authoritative sourcing, and structured data implementation than traditional SEO content, increasing per-piece production costs by 30-50% ¹. Organizations maintaining constant content volume while shifting toward GEO face budget pressure, while those maintaining constant budgets must reduce content volume, potentially sacrificing traditional SEO coverage and traffic.

Solution:

Implement tiered content strategies with differentiated production standards based on strategic value ¹. Designate 20-30% of content as "premium GEO-optimized" with comprehensive expert input, rigorous sourcing, and advanced structured data justifying higher production costs, focusing this investment on highest-authority topics where AI citation delivers maximum value. Maintain 50-60% of content as "hybrid optimization" meeting solid quality standards serving both channels without premium investment. Allocate 10-20% to "traditional SEO-focused" content for commercial queries where AI interception remains low and production efficiency matters more than citation-worthiness. This tiered approach optimizes resource allocation rather than applying uniform standards across all content. Additionally, leverage existing high-performing content for GEO enhancement rather than creating entirely new assets, implementing structured data and authority improvements on proven pieces. Explore efficiency improvements through AI-assisted content production tools that reduce costs while maintaining quality, enabling higher GEO standards without proportional budget increases.

Challenge: Rapid Evolution and Uncertainty

The GEO field evolves rapidly with each AI model update and search engine innovation, creating uncertainty about best practices and optimal tactics ¹²⁶. Strategies effective with current AI systems may become obsolete with next-generation models, while measurement frameworks require constant adaptation. This volatility complicates long-term planning and creates risk of investing heavily in approaches that quickly become outdated.

Solution:

Adopt agile allocation approaches with quarterly reviews and predefined rebalancing triggers rather than annual planning cycles ¹. Maintain strategic flexibility by avoiding over-investment in proprietary tools or tactics specific to current AI implementations, instead prioritizing foundational improvements (authoritative content, expert credentials, structured data) likely to remain valuable across model generations. Implement continuous learning programs monitoring research publications, industry developments, and experimental results to rapidly incorporate emerging best practices. Establish experimentation budgets (5-10% of total allocation) dedicated to testing new tactics without risking core programs, creating organizational capability for rapid adaptation. Build diversified GEO strategies spanning multiple AI platforms (ChatGPT, Google SGE, Bing Chat) rather than optimizing exclusively for single systems, reducing vulnerability to platform-specific changes. Frame GEO investment as capability building and strategic positioning rather than guaranteed returns, setting stakeholder expectations appropriately for an evolving field where tactics require constant refinement.

References

1. Semrush. (2024). Generative Engine Optimization. https://www.semrush.com/blog/generative-engine-optimization/
2. Search Engine Land. (2024). Google Search Generative Experience SGE Guide. https://www.searchengineland.com/google-search-generative-experience-sge-guide-430506
3. Ahrefs. (2024). SEO Statistics. https://ahrefs.com/blog/seo-statistics/
4. Google Developers. (2025). Introduction to Structured Data. https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data
5. arXiv. (2023). GEO: Generative Engine Optimization. https://arxiv.org/abs/2311.09735
6. Semrush. (2024). SEO Trends. https://www.semrush.com/blog/seo-trends/