This is your blueprint for staying visible, clickable, and trusted across Google’s evolving AI ecosystem.

Ranking Factors for AI Mode & Google Search Experience

How AI Mode Works

AI Mode is powered by a customized version of Gemini 2.5, Google’s most intelligent available model. This model is capable of thinking and reasoning through an in-depth thought process before responding, resulting in enhanced performance and improved accuracy.

• AI Overviews + AI Mode use query fan-out: They break a single question into many sub-queries to find the most relevant, high-quality information across the web.
• Deep Search can generate expert-level, cited reports, favoring multi-source, semantically rich pages.
• Real-time multimodal capabilities: AI Mode integrates live visual search through Project Astra, allowing users to have conversations about what they see using their camera.

Content Strategy for AI Overviews & Deep Search

Google’s shift to AI Overviews and Deep Search fundamentally changes how your content is discovered and cited. Instead of returning a list of links, Google is now using Gemini to interpret intent, break queries into subtasks, scan hundreds of sources, and synthesize a cohesive answer.

This changes your job from “get ranked” to “get cited.”

What Google AI is Optimizing For

1. Relevance and specificity
2. Originality and depth of insight
3. Authoritativeness, especially from niche experts
4. Semantic alignment with related subqueries

How to Optimize Your Content

Write for real human intent: Don’t just target keywords. Reverse-engineer full user prompts and sub-questions using forums like Reddit, Quora, and People Also Ask to understand how people phrase real questions.

Structure for skimming AND synthesis: Use subheadings, bullet points, summaries, and data boxes to make it easy for Google to extract meaningful text chunks.

Go deeper than summary-level advice: Offer frameworks, decision matrices, pricing breakdowns, firsthand experiences, expert quotes, or user-generated insights. Additionally, including expert quotes and firsthand experiences improves your brand authority and expertise.

Answer the follow-up question: If a user asks, “Is creatine good for runners?”, include the science behind it, usage tips, side effects, alternatives, and expert opinion. This way, you can cover both the explicit and the implicit user intents.

Create data-backed, expert-level content: AI Overviews are more likely to link to content with citations, named authors, data sources, and external references.

Technical AEO for AI Mode Retrieval

Technical accessibility is the price of admission. Googlebot and Gemini need to find, access, and interpret your content without friction.

Top 7 Must-Haves

1. Pages return 200 status codes (avoid soft 404s, 3xx redirects, or errors)
2. No robots.txt blocking Googlebot or Google-Extended
3. Meta tags like noindex, nosnippet, or data-nosnippet are used intentionally
4. Consistent canonical tagging to prevent duplicate content conflicts
5. Mobile-first design that loads fast and works across screen sizes
6. Proper use of headers (<h1> to <h3>) to reflect content hierarchy
7. Descriptive titles and meta descriptions that align with user intent and long-tail queries

Bonus Tips for AI performance:

• Semantically structured content with NLP-friendly phrasing
• Longer-tail topic pages that cover a full topic cluster rather than just a single keyword
• Embedded answers to FAQs, comparisons, and pros/cons to help LLMs extract structured information

Structured Data & Multimodal Optimization

AI Mode and Overviews rely heavily on structured data to surface and summarize rich content. As Google adds capabilities like visual live chat, virtual try-ons, and financial data graphs, structured data becomes essential.

Structured Data Best Practices

Structured data implementation provides search engines with the context they need to properly understand and showcase your content. Implement schema.org types that align with your specific business needs, including:

• Product markup for e-commerce sites,
• Service schema for professional offerings,
• Article markup for blog content,
• FAQPage schema for support sections,
• Event markup for upcoming activities,
• LocalBusiness for location-based services
• Recipe schema for food-related content etc.

Quality control is essential once you begin implementing structured data across your site. Validate every markup addition using Google’s Rich Results Test to ensure proper implementation, and maintain strict consistency between your schema content and what visitors actually see on the page. Mismatches can result in search engine trust penalties.

Enhance your credibility signals by including author, datePublished, and publisher information wherever applicable. These elements help establish authority and can positively impact how your content performs in search results.

Tips for Multimodal Search Optimization

• Use original images with descriptive, natural-language alt text
• Compress and size images for fast loading
• Add short-form video explainers or walkthroughs
• Upload video transcripts for better semantic extraction
• Keep Google Merchant Center product data and Business Profile details up-to-date and optimized

This matters more now that AI Mode includes Search Live, which lets users point their camera at an object or visual and ask live follow-up questions.

AI-Generated Content Guidelines & Trust Signals

Using AI to write content is not banned. But using AI to flood Google with thin, low-effort pages is grounds for a penalty under their scaled content abuse policy.

Follow These 4 Key Principles

• Don’t mass-publish similar pages that differ only by city/product/category name
• Add real human context and editing to every AI-assisted piece
• Disclose automation where relevant, such as:
  • Author notes
  • Image metadata (using IPTC DigitalSourceTypeTrainedAlgorithmicMedia)
  • Page footers with transparency disclaimers
• Use generative AI to structure, not replace, human creativity. Think: first draft assistant, not auto-publisher.

Metadata and Markup Hygiene

Every page requires custom <title> and <meta description> tags that accurately represent its content and purpose. Your structured data should be descriptive and consistent across all implementations while staying compliant with search engine policies to avoid penalties.

Enhance your markup with additional context signals where relevant, including isAccessibleForFree, inLanguage, and contentLocation tags. These additional data points help search engines better understand and categorize your content for more precise targeting.

How to Measure Performance Beyond CTR

Clicks are a fading metric in the AI search era. Google says AI Overviews drive “higher-quality clicks,” but volume is down and continuing to decrease

Key Metrics

• Conversion rate (form fills, purchases, subscriptions)
• Session depth (pages per visit)
• Time on site and bounce rate
• Return visits and retention
• Lead quality, not just quantity

Tools

• GA4 + Looker Studio dashboards for engagement segmentation
• Heatmaps (Hotjar, Microsoft Clarity) to analyze content friction
• First-click vs. last-click attribution modeling to evaluate AI-driven journeys
• Server-side tagging to capture more reliable data, especially with AI agents acting on behalf of users
• AEO platforms like Goodie AI to monitor visibility, measure performance, and take actions across AI search platforms

Next-Level Tactics for Brand Visibility

AI Overviews and AI Mode pull from a wider range of sources than classic Search. Your brand’s presence across the entire web determines your visibility.

Become an Entity, Not Just a Website

Ensure you build consistent profiles across Wikipedia, Crunchbase, LinkedIn, GitHub, YouTube, and press articles. Using a well-rounded strategy mitigates the chances of incorrect information being sourced and improves visibility through presence. Connect identities through sameAs schema and keep your About Page structured, semantic, and detailed.

Use Semantic Saturation

Build strong associations between your brand and high-intent phrases by consistently positioning your brand name close to the terms that matter most to your business. Create comprehensive, glossary-style content that defines and explores the terminology you want to own within your industry space.

Structure your content using hub-and-spoke topic clusters that demonstrate your domain expertise to AI systems. This approach helps large language models understand the breadth and depth of your authority across interconnected topics, making you more likely to be referenced as a credible source.

Get Mentioned in Credible Sources

Expand your digital footprint through strategic outreach to high-authority blogs, industry publications, podcasts, and newsletters within your sector. Position yourself as a thought leader by contributing to guest articles or participating in expert roundups that showcase your expertise to new audiences.

Build citation momentum across platforms where professionals gather and share insights, including Quora discussions, relevant Reddit communities, LinkedIn thought leadership posts, and Substack publications. These mentions create the reference signals that AI systems use to assess credibility and authority.

Optimize for How People Ask, Not How They Search

Instead of optimizing for traditional keyword phrases, design landing pages that directly answer the conversational questions users actually ask AI systems.

Rather than targeting “CRM for small SaaS,” create content that responds to natural language queries like “What’s the best CRM for small SaaS startups in 2025?” Apply this same approach across different content types, such as creating pages that answer “How to get verified on Instagram without being famous” instead of targeting “Instagram verification,” or “Compare budget electric SUVs with over 300-mile range” rather than “electric SUV comparison.”

These natural language patterns reflect how users communicate with AI assistants and search for information in an increasingly conversational search environment.

Future-Proofing: Agentic Search & AI Actions

Google AI Mode is not just answering questions. It’s now doing things on behalf of users:

• Booking reservations
• Purchasing tickets
• Comparing SKUs
• Surfacing local availability and pricing

If You Want Your Business to Be Selected by AI Agents

Make sure to keep your Google Business Profile up-to-date. Ensure you use availability, price, and action structured data. Additional best practices to improve selectability are to integrate with booking and ticketing partners such as:

• Resy
• OpenTable
• Vagaro
• Ticketmaster
• StubHub

Lastly, to automate your process and simplify updates, use a public API or structured feeds so Google can access real-time inventory

Anticipate Changes in User and AI Agent Behavior

Prepare for the shift toward conversational commerce, where users make specific requests like “find me a black turtleneck under $50, size M, in stock near me” rather than browsing product categories.

Similarly, LLM-driven agents will handle complex, multi-step tasks such as “book the closest available dentist for Thursday 4 pm, under $150,” requiring businesses to structure their data and booking systems for AI accessibility.

We’re Already in the Future of Search

LLMs are shaping what users see, trust, and act on. To stay visible and drive organic growth, your content must be designed for humans AND for generative engines.

This means shifting from keyword targeting to intent mapping, prioritizing organic conversions over vanity traffic metrics, and creating structured, original, expert-level content that demonstrates clear authority. Treat technical SEO as your foundational layer while embracing structured data and multimedia formats that AI systems can easily parse and reference. Focus on deeper engagement metrics that reflect genuine user value, and prepare for a world where AI agents increasingly determine which brands get discovered and which get overlooked.

The rules of search are being rewritten, but the opportunity is enormous. The brands that adapt fastest, those who earn visibility through value, clarity, and semantic strength, will dominate the AI-first era of discovery.

The post AI Mode Optimization Guide: Strategies for Google AI Search Experiences appeared first on NoGood™: Growth Marketing Agency.

OpenAI’s rollout of ChatGPT Shopping marks a platform shift in eCommerce. Instead of browsing on Google or Amazon, users are now turning to LLMs like ChatGPT to get personalized, intelligent product recommendations. This guide is for consumer brands, DTC operators, and digital commerce teams ready to optimize for this new AI discovery channel. 

We’ll walk through how ChatGPT Shopping works, what factors drive product visibility, and how to optimize your product data and presence for AI commerce.

Why ChatGPT Shopping Matters for Brands

The launch of ChatGPT Shopping signals a larger shift: AI answer engines are becoming the new front doors for product discovery.

When a user types something like “best dress shoes under $200” or “eco-friendly cookware set for induction stoves,” ChatGPT doesn’t send them to a list of links. It generates direct product recommendations – complete with prices, descriptions, review highlights, and merchant links – right inside the conversation.

No ads. No sponsored listings. No pay-to-play – at least for now.

Product recommendations are based on:

• User intent
• Structured metadata
• Contextual relevance

For brands, this creates an entirely new surface for visibility and performance. Think of it as a new organic channel: AI search commerce. This also means adding a new playbook.

How ChatGPT Shopping Works

When a user enters a shopping-related query like “best lightweight stroller for travel”, here’s how ChatGPT processes it:

1. Intent Analysis

ChatGPT classifies the query as shopping-focused and uses semantic understanding to categorize the user’s need (e.g., comparison, deal hunting, browsing).

2. Context is Matched

If memory is enabled, ChatGPT may use past preferences (e.g., style preferences, price sensitivity). It also incorporates instructions in the query (“under $100,” “for wide feet”) to refine results.

3. Product Data is Pulled

ChatGPT then pulls structured product data from sources like Bing Shopping, Shopify, and merchant feeds. It also gathers reviews from across the web to incorporate into results.

4. AI Generates Product Cards

Each product includes a title, AI-written summary, price, availability, review highlights, and merchant link. Labels like “Top Pick” or “Budget-Friendly” are generated by AI based on review content, not paid promotion and placements.

5. Links Are Offered to Merchant Sites

Users can click to view and buy products directly from merchants. OpenAI doesn’t take a commission or rerank listings based on commercial agreements.

ChatGPT Shopping Might Not Display for Various Reasons

The ChatGPT Shopping feature is still rolling out and might not generate for every shopping query yet. When I asked why ChatGPT shopping didn’t show up for my product query, here are the explanations I got:

What Influences Product Rankings in ChatGPT

There is no pay-for-placement model. Products show up based on:

• Trust & Safety: Products flagged for low quality or lack of social proof may be filtered out.
• Structured Metadata: Title, description, image, brand, SKU, price, availability, GTIN, size, material (the richer and more complete the data, the better).
• Review Signals: Volume and quality of user feedback; summaries are generated by AI. Products with consistent praise or clear pros/cons are prioritized.
• Intent Relevance: Semantic alignment with the query (e.g., “compact,” or “quiet”).
• Schema & Feed Quality: Incomplete or outdated metadata can cause exclusion.

5 Ways to Optimize Your Products for ChatGPT Shopping Visibility

Welcome to the world of AI Search Optimization. Here are 5 essential strategies to get your products ranked and displayed.

1. Allow ChatGPT to Crawl Your Site

Update your robots.txt file to allow OpenAI’s crawler:

User-agent: OAI-SearchBot

Disallow:

Also, implement the llms.txt file to define access for LLM more broadly.

2. Structure Your Product Data Correctly

Whether you’re on Shopify, Magento, or headless, ensure your product feeds include these key attributes:

Use JSON-LD for schema markup. Validate it using Google’s Rich Results Test, but note that AI search engines may require broader attribute coverage than traditional SEO.

3. Use Conversational Copy

AI models prefer clarity. Rewriting titles and PDPs for semantic accuracy improves your chance of getting surfaced.

Examples:

• Instead of “Techwear Pants V3,” use “Waterproof Breathable Hiking Pants.”
• Add FAQ content like: “Are these pants machine-washable?” or “Do they run large?”

4. Leverage Review Sentiment

ChatGPT summarizes real user reviews. Your job is to:

• Ensure your reviews reflect usage and benefits
• Highlight sentiment keywords (“lightweight,” “easy to fold”) in your PDPs
• Get reviews published on third-party sites (Reddit, blogs, YouTube). Broader digital footprint = higher LLM visibility.

5. Measure ChatGPT as a Channel

Attribution is evolving. Here is what you need to start tracking:

• Add UTM tags to product URLs with utm_source=chatgpt.com
• Track GA4 events from ChatGPT traffic
• Include post-purchase surveys asking “How did you find us? (Google, ChatGPT, Perplexity, etc.)”
• Use AI-specific promo codes (e.g., “GPT10”)

The Goodie CHAT Framework™

Our proprietary AEO (answer engine optimization) framework for Shopping Optimization in AI search platforms helps you align with how ChatGPT ranks and recommends products.

Chat Framework Breakdown

BONUS: AEO Periodic Table of AI Search Commerce Visibility Factors

What Product Types Are Winning in ChatGPT Right Now

As of May 2025, ChatGPT Shopping appears strongest in these categories:

• Fashion and footwear
• Beauty & personal care
• Home goods (decor, storage, cookware)
• Consumer electronics & gadgets

These verticals tend to have rich structured data, strong review footprints, and frequent user-driven queries.

What’s next? Expect expansion into:

• Specialty foods
• Health & supplements
• Furniture
• B2B SaaS tools (think: “best CRM for small businesses”)

The Competitive Edge: How to Win in AI Search Commerce

Invest in Metadata Infrastructure

Treat your product data like a core asset. Clean, enrich, and normalize it across every listing. Use tools like:

• Goodie AI for end-to-end AEO, including brand and product monitoring and optimization
• Hypotenuse AI for deep schema review and enhancement
• Feedonomics for managing product feeds
• Contentful or Sanity.io for structured CMS delivery

Build AI-Ready Brand Presence

Your product reviews, mentions on Reddit, presence in YouTube roundups, and media citations feed into how LLMs perceive your brand. AI visibility is not just on your PDPs—it’s everywhere you’re mentioned.

Shift Your SEO Team’s Mindset

It’s no longer about ranking #1 on Google. It’s about being the right answer in a zero-click environment. That requires:

• Understanding how LLMs ingest, interpret, and rerank product data
• Working cross-functionally across product, content, engineering, and PR
• Moving from keyword strategy to intent modeling

How ChatGPT Shopping Compares to Google and Perplexity

To help you decide where to focus your optimization efforts and understand how AI shopping tools compare, here is a deeper look into how ChatGPT, Google Shopping, and Perplexity’s Shop Like a Pro stack up:

What Consumer Brands Should Do Right Now

• Audit your structured product data and schema
• Monitor and build an optimization engine using a native AEO platform
• Join OpenAI’s merchant feed beta
• Reformat PDPs for clarity, benefits, and AI readability
• Train your team on AI search commerce basics
• Build dashboards to track ChatGPT traffic and performance

Where We Go from Here: The Future of AI Search Commerce

We are only in the early innings of AI product discovery. Here’s what the next 12–36 months of AI search commerce could look like:

1. AI-Native Shopping Assistants and Agents Will Become Ubiquitous

Tools like OpenAI’s Operator or Anthropic’s Claude agents will evolve into fully personalized shopping concierges that browse, compare, and buy on your behalf—automating entire decision cycles. Brands must optimize not only for visibility but for interpretability and transactional readiness.

2. Sponsored Listings & Commercial Models Will Arrive

Though ChatGPT and Perplexity currently have no paid placement model, it’s inevitable. Expect native sponsored product slots and affiliate integrations that look and feel organic. Brands should prepare for a hybrid world where paid + organic optimization coexist inside conversational environments.

3. Multimodal and Voice Shopping Will Emerge

Soon, consumers will ask their AI assistant to “find a backpack like this photo” or “buy me the same vitamins I ordered last time.” Shopping via voice and images will increase the need for structured product data and consistent labeling across channels.

4. Product Discovery Will Be Layered Into Social and Messaging Apps

With ChatGPT integrating into platforms like WhatsApp and iMessage, the AI shopping layer will embed itself directly into how people communicate. Brands that seed conversations and are prepared to respond to AI-curated product intent will lead.

5. AI Visibility Will Require Cross-Channel Consistency

It won’t be enough to optimize your PDP. AI systems pull from schema, reviews, Reddit, TikTok, YouTube, and third-party commerce APIs. If your product content isn’t consistent, up-to-date, and high quality across platforms, you risk invisibility.

6. AEO is the new SEO

As AI becomes the gateway to product discovery, Answer Engine Optimization (AEO) will sit alongside SEO, performance marketing, and retail media in the brand playbook. Forward-thinking teams will invest in AEO infrastructure, monitoring, and attribution.

In short, The winners in AI search commerce will be those who think like machines, act like publishers, and perform like retailers.

Final Word: AI Is the New Shelf

ChatGPT Shopping is more than a feature. It’s a paradigm shift. Consumers no longer need to Google and sift through 10 tabs. They ask. The AI answers.

And if your product isn’t structured, described, or visible in the right way? You won’t be part of the conversation.

In the new era of AI search commerce, visibility isn’t earned by backlinks or bidding, it’s earned by clarity, trust, and structured product data.

If you want your brand to win on the new digital shelf, the time to optimize is now.

The post ChatGPT Shopping: Definitive AEO Guide to AI Search Commerce appeared first on NoGood™: Growth Marketing Agency.

Recently, we’ve seen a rapid rise of LLMs and AI search, but in contrast, community and social search are powered by human ingenuity and connection. This marks a new paradigm where authenticity, community validation, and real-time engagement form the cornerstones of brand visibility and consumer trust today. 

The Trust Deficit and the Rise of Community Validation

Recent data highlights a significant erosion of trust between consumers and brands, emphasizing a critical need for transparency and ongoing engagement. Consumers today are not just skeptical — they actively seek validation from trusted communities and peers. 

This shift is evident in changing search behaviors, with many adding “Reddit” to the end of their Google searches or turning to niche search engines, social platforms, and AI-powered answer engines for more authentic insights. User-generated reviews, trusted creator partnerships, and peer-to-peer recommendations are now pivotal in guiding consumer decisions, offering brands opportunities to rebuild trust through authentic community conversations and validated content.

Brands that authentically plug into conversations and use UGC effectively find themselves well-positioned to build credibility. For instance, relatable customer reviews and non-forced testimonials have consistently proven more persuasive than branded advertisements, directly impacting purchase decisions. Partnerships with creators and influencers who effortlessly align with a brand’s values further amplify this trust, forging deeper, more authentic consumer relationships.

This trust deficit is rampant in the creator economy as well. Creators remain central to brand discovery, but authenticity is paramount. Modern consumers readily discern between genuine endorsements and overtly promotional messaging, valuing the former significantly more. Approximately 71% of consumers report a higher likelihood of purchasing products endorsed by trusted influencers. Consequently, brands must focus on aligning with creators whose values and messaging resonate authentically with their target audiences.

Successful creator partnerships prioritize long-term relationships, fostering genuine connections rather than one-off campaigns. Brands that adopt this approach gain sustained credibility and deeper consumer engagement, capitalizing on influencers’ ability to drive meaningful, authentic interactions.

The Fragmentation of Search: The Rise of Social Search

Conventional search engines like Google and Bing no longer monopolize how consumers discover brands. In addition to AI search platforms, social platforms like TikTok, Instagram, LinkedIn, and YouTube have emerged as significant channels for brand exploration and discovery. Approximately 78% of global internet users utilize social media for product research, and notably, around 40% of Gen Z adults specifically prefer these platforms for brand discovery.

With today’s fragmented search landscape, almost anything can be a search engine. That’s why there’s also a rise of specialized search engines and vertical-specific platforms that help users get better answers, faster.

Instead of turning to Google as the source of truth for all search queries, users, particularly younger Gen Z consumers, are using Beli or Yelp to find restaurants, Zocdoc to find doctors, Expedia to find hotels, and Spotify to discover new music. This shift underscores the need for brands to rethink content strategies, optimizing not just for keywords but also for relevant visibility and engagement within social search contexts.

Social search thrives on authentic, visually engaging, and rapidly consumable content — short-form videos, concise storytelling, and visually rich experiences that align with user browsing behaviors. But beyond seeking direct answers, users increasingly turn to social search for community perspectives and nuanced opinions that help shape their own viewpoints.

Brands that understand and master these new search behaviors stand to significantly improve their discoverability and foster deeper community engagement by tapping into conversations that reflect collective experiences, not just transactional queries.

Generative AI Meets Social Search: The Future of Search Experiences

The intersection of generative AI with social search is reshaping how users discover and interact with brands. Platforms such as TikTok increasingly function as dynamic, personalized search engines driven by AI algorithms that prioritize engaging, authentic, and community-validated content.

An April 2024 survey found that 45% of Gen Z users prefer TikTok over Google for discovering new products and information, highlighting a substantial shift towards interactive, AI-enhanced search experiences. TikTok doubled down on this shift by expanding its search capabilities, more recently adding search highlights by providing summaries to top-ranking videos and allowing search ads on the platform. 

When you think about it, this makes the news of Perplexity AI’s interest in acquiring TikTok no surprise, as it underscores a strategic alignment: leveraging TikTok’s extensive community-driven data to enhance AI search and recommendation systems. This probably offers the best example of how generative AI meets social graph. These integrations point toward a future where social content significantly informs generative AI capabilities, delivering highly personalized and credible search experiences.

Social Content as Retrieval Sources for AI Search & LLMs

According to Goodie AI’s recent AEO periodic table, social content – including consumer sentiment, mentions, and reviews – has become invaluable for AI search engines and LLM retrievals. They’re key variables in deciding a brand’s visibility.

Platforms like Reddit, known for authentic, unfiltered user-generated content, provide rich, nuanced data that significantly enhances the authenticity and relevance of AI-generated responses. Brands actively engaging in such communities not only increase their visibility but also influence AI conversations about their products and brand narrative.

To put it in context, Reddit.com saw a 1,328% increase in SEO visibility on Google between July 2023 and April 2024, according to Sistrix data cited by Amsive. The major change that year was the introduction of Gemini and AI overview, which favors Reddit as a source to validate claims and user sentiment.

By tapping into real-time social content, AI search experiences can offer depth and accuracy, positioning brands effectively within genuine consumer conversations. This integration further reinforces consumer trust by ensuring search results reflect authentic community feedback rather than solely brand-produced narratives.

Leveraging Community-Driven Content for Enhanced Discoverability

Actively engaging with community-driven content — customer reviews, forums, and social media interactions — is essential for brands aiming to boost discoverability and authenticity. Encouraging and amplifying customer experiences helps brands organically improve their visibility across social platforms and search results.

Community engagement itself has become a key strategy for brands’ social media teams, as comment sections become more and more of a space for brands to participate in relevant conversations and show off their unique tone of voice or point of view.

Brands successful at facilitating vibrant community interactions through dedicated forums, discussion groups, UGC content or micro-community platforms, enjoy not just increased visibility but also heightened trust and loyalty from their customer base. This active involvement significantly enhances both brand reputation and reach.

Case Studies of Community & Social Search Success Stories

Duolingo’s Community Powering Language Learning Ecosystem

Duolingo revolutionized language learning discovery by creating a multi-layered community ecosystem that drives both engagement and organic growth. Their approach centered on transforming traditional education into a community-validated experience through several key initiatives:

• The Duolingo Forums: A dedicated space where learners discuss language nuances, cultural contexts, and learning strategies, generating over 25,000 monthly posts that serve as rich retrieval sources for AI-powered search.
• User-Generated “Stories” Feature: Community members create and share language learning narratives, resulting in 3.2x higher retention rates for users who engage with community content.
• TikTok-Optimized Learning Snippets: User-generated language tips that drove a 189% increase in organic app discovery, particularly among 18-24 year olds.

Notably, Duolingo found that learners who discovered the platform through authentic community content demonstrated 47% higher daily engagement and 2.8x longer lifetime value compared to users acquired through traditional advertising.

The company’s approach directly aligns with the evolution of community-driven search by prioritizing authentic peer validation, leveraging user-generated content across multiple platforms, and creating an ecosystem where community insights directly inform product development, creating a trust-building feedback loop.

Lululemon’s Product-Led Community Strategy

Lululemon transformed its approach to product discovery by creating a robust community feedback loop centered around their mobile app. By implementing community product ratings, authentic user-generated content, and direct integration with their ambassador network, Lululemon achieved remarkable results:

• 72% increase in product discovery through community recommendations
• 4.8x higher conversion rate for products with user-generated imagery compared to professional photography alone
• 68% of new customers reported discovering the brand through peer recommendations or community content

The company further leveraged this strategy by integrating community feedback directly into product development, creating a virtuous cycle where customer input shapes future offerings, strengthening loyalty, and amplifying organic discovery.

Glossier’s Reddit-First Approach

Glossier pioneered a “Reddit-first” strategy that prioritized authentic community engagement over traditional marketing. Rather than simply monitoring mentions, the brand actively participated in skincare and beauty subreddits, providing valuable expertise without overtly promoting products. This approach yielded:

• 215% year-over-year increase in organic search traffic from community-driven platforms
• 3.2x higher customer retention rates for customers acquired through community channels
• Significant improvement in AI-powered search visibility as their content became a trusted retrieval source

Most notably, when Glossier launched new products, they saw 40% of first-week sales come directly from Reddit community members, demonstrating the powerful conversion potential of authentic community engagement.

Oatly’s TikTok Community Strategy

Oatly reimagined brand discovery by prioritizing TikTok’s community-driven search ecosystem. Instead of creating overtly branded content, they empowered customers and micro-creators to showcase authentic product experiences, resulting in:

• 11 million user-generated impressions in a single quarter
• 189% increase in brand discovery among Gen Z consumers
• 5.7x ROI compared to traditional digital marketing channels

Critically, Oatly discovered that when consumers discovered their products through authentic TikTok content, they were 3.4x more likely to become repeat purchasers compared to those who discovered the brand through paid advertising, highlighting the trust premium associated with community-validated discovery.

Emerging Trends Shaping Social and Community Search

Several additional trends merit attention for brands navigating next-gen discoverability:

• Micro-Communities: Platforms such as Discord and Substack allow brands to foster close-knit, highly engaged communities, offering targeted visibility and deeper trust-based relationships. 
• Searchtainment: Younger audiences view search as entertainment, consuming informative yet visually engaging content. Brands embracing this style align closely with user expectations, boosting engagement.
• The Authenticity Paradox: Brands face the delicate balance of demonstrating authenticity without appearing contrived. Transparency and genuine behind-the-scenes content resonate deeply, enhancing consumer trust.
• Peer-to-Peer Recommendations: Direct peer recommendations remain the most trusted brand endorsements, moving beyond traditional reviews into intimate digital spaces like WhatsApp and private messaging platforms.
• Co-Creation with Communities: Brands actively involving their audiences in content creation, product design, and strategic decisions foster higher trust and alignment with consumer needs, driving brand advocacy.

By strategically integrating these insights and tactics in GTM motions, brands can adeptly navigate the shifting landscape of discoverability, turning consumer skepticism into authentic engagement and enduring brand loyalty.

Community and social search not only redefine brand discovery but also elevate trust, connection, and visibility, setting the foundation for future marketing and distribution and the war for consumer attention. 

The post The Future of Brand Discovery Between Social Search & Generative AI appeared first on NoGood™: Growth Marketing Agency.

In traditional organic search and SEO, most strategies revolve around climbing rankings on search engine results pages (SERPs). But with Generative Engines like ChatGPT, Perplexity, and Gemini are driving more and more search behavior, users are asking questions in a conversational way and receiving synthesized, context-rich answers that often eliminate the need to click on external links.

The concept of Generative Engine Optimization (GEO) emerges as the strategic framework to ensure your brand is both discoverable and cited by these new highly intelligent engines. This is an area that we have research and written about extensively. To succeed in this new landscape, brands need a playbook that weaves together the sustainable SEO fundamentals as well as AI Search insights.

This guide will walk you through why GEO matters, how it differs from standard SEO, what ranking factors drive AI-based visibility, and how to implement effective GEO strategies and measure impact.

Why GEO Matters More Than Ever

Generative AI has turned the concept of “search” on its head. Users no longer need to browse through lists of blue links; advanced LLMs can synthesize meaningful answers in real time, often without sending the user to an additional website. This answers-first paradigm carries big implications for brands and marketers:

• Visibility is more elusive: Simply ranking #1 on Google doesn’t guarantee you’ll be featured in an AI-generated response.
• Users expect concise, context-rich replies: They might never see your well-optimized landing page if the AI chat interface provides everything they need.
• Multi-turn conversations: Users can refine queries on the fly and ask follow-up questions, shifting from “What are the best running shoes?” to “Which brand is eco-friendly?” and “Where can I buy them locally right now?”—all in a single session.

In this environment, your brand must be recognized by AI engines as authoritative, trustworthy, and contextually relevant. Generative Engine Optimization (GEO) ensures that you not only exist in a search index, but also become a central piece of the conversation—that is, the conversation between the user and the AI model.

The Consequences of Not Adapting

Brands that ignore GEO risk fading out of AI-driven ecosystems, losing mindshare and, ultimately, conversions. Traditional SEO alone is no longer sufficient; even if your page ranks well on Google, AI results may overshadow standard SERPs or reduce click-through rates. Thus, investing in GEO now is an investment in future-proofing your brand’s discoverability.

What Is Generative Engine Optimization (GEO)?

Generative Engine Optimization (GEO) is the practice of influencing and optimizing how AI-driven search systems—especially those powered by LLMs—access, interpret, and include your content in their automatically generated answers.

While SEO focuses on ranking in traditional SERPs, GEO goes a step further. With GEO, your objective is to:

1. Embed your brand, data, and expertise into the knowledge base and training sets of AI models.
2. Align your content structure and signals so that real-time AI search engines (e.g., Perplexity, Bing Chat, ChatGPT Search) see you as a relevant, high-authority source.
3. Encourage AI to cite or reference your brand and offerings in the final, synthesized response it delivers to the user.

It’s a holistic approach that blends technical SEO with content quality, brand authority, and community engagement, all under the lens of how advanced AI systems process and produce information.

Generative Engine Optimization (GEO) vs. Answer Engine Optimization (AEO): What’s the Difference?

GEO and AEO are both strategies aimed at boosting the visibility of your content online in AI-driven search functions, but AEO encompasses additional platforms beyond those targeted by GEO practices, and GEO is geared toward ensuring your content is one of many sources used in the synthesis process that enables AI platforms to provide succinct and summarized answers.

Both AEO and GEO methodologies strive to provide concise, direct answers to user queries to increase the chance of appearing in AI-generated results. With GEO, as we’ve discussed, this refers to AI-driven search systems, and particularly LLMs. With AEO, this can also mean AI Overviews or other AI-powered aspects that are beginning to populate more traditional search engines.

At the foundation of both AEO and GEO is understanding user intent, incorporating relevant keywords, structuring content effectively to align with algorithms and AI crawlers, and using clear language throughout your content. The same techniques are used for both AEO and GEO since all generative engines are a type of answer engine.

Market Growth and User Adoption of Generative Search and LLMs

To understand the urgency behind GEO, consider the rapid adoption of LLMs and AI-based search technologies:

• ChatGPT reached 100 million monthly users within two months of launch, making it one of the fastest-growing consumer applications in history. In November of 2024, it had 3.8 billion visits, showing that initial hype is transitioning into mainstream usage.
• According to a recent survey from investment banking firm Evercore, 8% of Americans use ChatGPT as their go-to search engine.
• A McKinsey survey in mid-2024 found that 65% of organizations now regularly use generative AI, up from just 33% the previous year. This aligns with the broad enterprise pivot toward AI-driven insights.
• Gartner projects that by 2026, 25% of all queries could shift from traditional search engines to AI-driven interfaces, reducing organic search traffic for many companies by up to 50%.

These trends highlight the explosive growth of AI search channels. If your brand is absent from these channels—or if your mentions are inaccurate—you could be missing out on millions of high-intent users.

What’s the Difference Between GEO and SEO?

At a glance, GEO seems like a natural extension of SEO. Both revolve around making content visible and authoritative, but the mechanics of each differ significantly.

One of the biggest contrasts is that SEO is built around competition for finite SERP positions (rank #1, #2, #3, etc.), whereas GEO is more about influencing what an AI engine “thinks” and “says” when it fields questions.

Because AI’s answers are generated dynamically—and often vary from user to user—traditional ranking trackers don’t apply. Instead, you might look at brand presence across AI responses, the quality of mentions, and how these mentions convert into brand searches or direct visits later.

GEO Optimization and Ranking Factors

AI systems weigh a combination of technical, content, brand, and engagement signals when generating answers. While each generative model has its own training and referencing logic, several overarching factors frequently determine whether (and how) your brand appears.

Content Quality & Context

Generative models thrive on detailed, high-quality content. If your pages offer superficial or repetitive information, an LLM may ignore them. Focus on:

• Topical Depth: Provide unique data, expert opinions, or original research.
• Contextual Relevance: Align content around real user questions and pain points, anticipating the queries people pose to AI.
• Readability & Structure: Format text with headings, bullet points, and concise paragraphs to make it easy for AI to parse.

Technical Accessibility & Structured Data

As with SEO, you must ensure your site is crawlable and indexed. You should:

• Include Schema Markup (JSON-LD): e.g., FAQ, Article, Product, and Author schema help AI understand your site.
• Ensure Open Access: Allow GPTBot and other AI crawlers if you want your data included in their training sets or live indexing.
• Optimize Site Speed & Mobile-Friendliness: Real-time AI crawlers assess site performance and may discard or deprioritize slow, clunky pages.

Entity and Brand Authority

LLMs generally rely on an entity-based understanding of content:

• Entity Confirmation: Ensure your brand is defined consistently across platforms, from social media to Wikipedia.
• Authority Cues: Publications, academic references, news mentions, and strong domain-level authority all indicate trustworthiness.
• E-E-A-T Alignment: Google’s emphasis on Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) also extends to how LLMs interpret your site.

Off-Page Signals & Community Mentions

Generative AI training can involve everything from social media posts to discussions on forums. Mentions in these spaces can shape how the model perceives your brand:

• Unlinked Brand Mentions: LLMs can learn about your product even from references without a hyperlink.
• Community Discussions: When your brand is actively recommended or debated in communities like Reddit or Quora, that can surface in AI’s knowledge base.
• Social Proof: High engagement on social posts and user-generated content signals real-world relevance.

Engagement & User Experience

While Google may or may not take bounce rates into account for rankings, AI systems are beginning to incorporate user feedback loops:

• Conversational Flow: If users repeatedly mention or engage with your brand in multi-turn queries, the AI may rank you higher or keep referencing you.
• Positive Sentiment: LLMs can parse sentiment in reviews or discussions to gauge brand favorability and will rank brands with positive sentiment higher than those more negatively regarded.

GEO Implementation Strategies: How to Get it Right?

Many of the most impactful strategies for GEO focus on developing and distributing high-quality content that aligns with and supports the authority of your brand. Think of your content as the backbone of your GEO strategy.

Research & Planning

To create content that resonates with your audience and will be relevant to their queries, you must start with research:

1. Discover User Questions: Map the long-tail, question-based queries people ask about your industry. Tools like AnswerThePublic, Semrush’s “Questions” feature, and even direct prompts to ChatGPT or Perplexity can yield potential user phrasing.
2. Identify AI Engine Types: Pinpoint whether your audience is more likely to use ChatGPT’s offline data or Bing Chat’s real-time engine. This dictates your approach to content freshness and domain authority.
3. Competitor Analysis: Ask generative engines about your competitors. Check how they appear in responses. Identify gaps you can exploit.
4. Brand Perception: Dig into how your brand is perceived by those who are searching in your niche. Understand what people are saying about your brand so you can grasp the current sentiment and figure out where you may need to make changes.

Content Creation & Structuring

What you include in your content and how you structure it directly impacts how LLMs are able to understand your relevance to and your expertise on a certain topic. Focus heavily on providing detailed, thoughtful, and original perspectives on important topics in your industry.

When structuring your content, you should:

• Provide Direct Answers: Start content pieces with a concise, “TL;DR”-style summary. AI often lifts these for quick responses.
• Use Clear Headings: Group subtopics with H2/H3 labels so LLMs can easily identify relevant sections.
• Include Original Data & Quotes: Studies show that LLMs prefer referencing content with statistics, quotes, or unique angles.
• Multi-Format Approach: Embed videos, infographics, or tables for depth; advanced models can incorporate them into answers.
• Incorporate FAQ Sections: Build FAQ sections, especially in longer content, to address common questions and make it easy for AI to pull relevant information.

Optimizing for Different Generative AI Engine Types

Training-Based Models (ChatGPT, Claude, Llama) rely on a set library of knowledge, usually with a cut-off date. To optimize for these models, consider long-term and evergreen approaches:

• Get your brand into widely crawled sources (news outlets, authoritative databases).
• Focus on timeless, high-authority content that remains valid well after the model’s training cutoff.

Real-Time & Hybrid Models (Perplexity, Bing Chat, Gemini, ChatGPT with browsing) can browse the internet as they’re processing a query to build the most relevant response. Focus on more immediate fixes and updates to optimize for these models, such as:

• Technical SEO Best Practices: Fresh, indexed content is essential; site speed, structured data, and mobile optimization are also critical for AI to quickly parse and synthesize information on your website.
• Timely Updates: Publish frequent updates, new research, or product info so the real-time crawler picks it up. Consistent and timely content publishing ensures you stay relevant in your particular industry.

Distribution & Community Building

Today with the rise of GEO, community building has become the new link building. Since LLMs are trained on a wide variety of data from all corners of the internet, make sure to amplify and take part into your brand discussions beyond your website:

• Active Forum Participation: Contribute meaningfully on Reddit, Quora, and niche communities.
• Social Media Seeding: Post share-worthy insights on LinkedIn or industry-specific networks.
• Guest Podcasts & Webinars: Voice your expertise in events that might be transcribed and then included in AI training sets.

Testing & Iteration

Many AI models are still being trained, new AI models are being released, and new features are being added to the ones that already exist. Keep close tabs on your GEO strategy implementation and test where necessary to ensure you’re seeing results:

• Prompt Experiments: Regularly ask ChatGPT, Gemini, Perplexity, etc. about your brand or key topics. Track if you appear and in what context.
• Refine & Re-Test: Adjust content structure or brand mentions and test again. This cyclical process is crucial for incremental GEO improvements.
• Collaboration with R&D: If your company has data scientists or partnerships with AI tool providers, collaborate to see how training sets are curated.

Case Studies and Practical Examples

Case Study 1: B2B SaaS Gains AI Mentions

A B2B SaaS company in the project management niche found they rarely appeared in ChatGPT’s offline knowledge. To remedy this, they:

• Partnered with Industry Blogs: They published joint reports with tech sites that had high domain authority.
• Increased Wikipedia Presence: They created a well-cited Wikipedia entry referencing their white papers.

Result: Within the next model update cycle, ChatGPT began including them in the top “best project management tools” references, boosting direct brand searches by 25%.

Case Study 2: eCommerce Brand Optimizes for Perplexity

An eCommerce retailer specializing in eco-friendly lifestyle products noticed Perplexity was growing in popularity among sustainability-minded consumers. To drive inclusion of their brand in Perplexity’s results, they implemented:

• Structured FAQ: They added custom FAQ schema with concise Q&A blocks to their product pages.
• Community Advocacy: They encouraged satisfied customers to share experiences on subreddits like r/ZeroWaste.

Result: In under two months, the brand appeared consistently in Perplexity’s “top eco-friendly stores” suggestions, correlating with an 18% bump in monthly revenue.

Case Study 3: Consultancy & Hybrid Systems

A consultancy wanted to rank in Google Gemini and also appear in ChatGPT’s real-time browsing variant. To achieve this, they focused on:

• Weekly Thought Leadership: They posted in-depth blog posts on trending management topics, ensuring daily crawls by Google.
• Citation Seeds: They proactively commented on Fortune 500 LinkedIn posts, dropping relevant data bites that earned them news coverage.
• Result: Gemini frequently included their quotes, and ChatGPT-with-browsing began citing their blog for “up-to-date leadership insights.”

How to Measure GEO and AI Visibility

Measuring success in GEO can feel elusive if you’re used to the more stable metrics of traditional SEO. While SEO performance may shift on a weekly or monthly basis, GEO is even more inherently dynamic—AI answers can shift from day to day, or even query to query.

Key Metrics

1. AI Citation Frequency
   • How often does the model mention or source your brand?
   • Does it list your domain as a reference or recommended link?
2. Brand Visibility in AI Overviews
   • Are you recommended in relevant top-tier queries? (e.g., “best enterprise software solutions”)
   • Does your brand appear in “buying guide” or “how-to” generative answers?
3. Post-AI Direct Traffic
   • When AI references your brand, do you see a spike in branded searches or direct visits?
   • Do you see an increase in brand + product name queries? This is a solid sign of AI-driven awareness.
4. Referral Data
   • Some AI chat interfaces provide clickable citations. Monitor referral traffic from these sources.
5. User Engagement
   • Keep an eye on lead volume, time on site, or newsletter sign-ups. Sometimes, AI references warm up leads before they even arrive.

Monitoring Tools & Methods

A strong GEO strategy requires established methods for measuring performance and impact of optimizations. Consider the following to track visibility:

• Prompt Testing: Manually check responses by asking the AI about your brand or relevant keywords.
• Brand Mention Tools: Invest in a tool from the new wave of software that specifically tracks brand references in generative AI outputs.
• Analytics: Use Google Analytics (and similar) to monitor direct traffic spikes; use Google Search Console to track brand search volume.
• Social Listening: Incorporate a tool that tracks social sentiment and discussions that could feed into AI training sets.

Goodie AI for Visibility Analysis

Goodie AI is an emerging platform that helps brands gain insights into how they show up in generative AI outputs. It offers:

• Real-Time Prompt Testing: The platform automates query testing across multiple AI engines (ChatGPT, Gemini, Perplexity).
• Citation Tracking: It logs whenever your brand is mentioned, along with the conversation context.
• Competitive Benchmarking: Goodie compares your brand’s generative presence to key competitors.
• Performance Dashboards: The platform includes a consolidated view of impressions, mention frequency, and potential referral traffic.

Integrating a tool like Goodie AI can streamline your entire GEO measurement process, giving you actionable data to refine your strategy over time.

Future of GEO and AI Search: Closing Thoughts

Generative AI is rapidly transforming how people search, research, and make decisions. Far from a temporary hype cycle, LLMs are already mainstream and improving continuously with each iteration. This means:

1. Data Quality & Freshness Will Become Essential: AI engines that rely on real-time data will increasingly factor in recency and reliability. Maintaining updated content is critical.
2. Deeper Personalization Is on the Horizon: Future AI systems may tailor their responses based on the user’s past interactions, location, or personal preferences. Optimizing for broader “personas” might become as important as keyword targeting once was.
3. Voice & Visual Inputs Will Blend With Text: As voice-based and image-based queries increase, GEO strategies will expand to cover multimodal content optimization.
4. Ethical & Trust Factors Will Be Increasingly Important: Misinformation and AI “hallucinations” remain hot-button issues. Brands that prove trustworthiness—through robust references, transparency, and verified credentials—will likely earn preferential treatment from AI systems looking to minimize errors.
5. Standardization Could Emerge: As generative AI matures, there may be regulations or standard practices for citing sources—akin to how search engines have guidelines for indexing.

The Bottom Line

Generative Engine Optimization isn’t just “SEO with a twist.” It’s a new frontier that intersects branding, content strategy, technical optimization, and community influence. In an era when user queries can be answered entirely within a chat window, ensuring that your brand’s perspective informs that answer can make the difference between winning a new lead or never being discovered at all.

By focusing on quality content, structured data, brand authority, and active community engagement, you position your organization to capitalize on the AI revolution—rather than be sidelined by it. Embrace GEO now to protect and amplify your brand’s digital visibility for the next decade and beyond.

The post Generative Engine Optimization (GEO): Strategies to Boost AI Search Visibility appeared first on NoGood™: Growth Marketing Agency.

Despite the hype, many marketers’ day-to-day hasn’t changed much from two years ago. We’re still designing, creating content, and spending time analyzing ad platform data, CRMs, and Google Analytics like we always have.

But amidst the AI noise, a new breed of marketer is rapidly emerging.

Introducing the 10X AI Marketer

In engineering, we often hear the term “10X engineer.” It’s usually used to describe a software engineer who is considered significantly more productive than their peers and performs at a level roughly ten times more effective than the average engineer. This concept was rarely applied to marketers. That’s largely because, until now, marketers haven’t had the tools to design and orchestrate large-scale systems the way engineers do.

But I believe this is the time for it.

With the advent and integration of AI, marketers have the chance to become 10X AI marketers, utilizing AI to supercharge and optimize their workflows for better outputs, faster.

In short, 10X AI marketers get 10 times the results in a fraction of the time without doing 10 times the work themselves—AI is doing the heavy lifting. Here’s what makes them stand out:

1. They Move Fast. They rapidly prototype ideas, spin up automations, and experiment with novel workflows. AI helps them iterate at lightning speed.
2. They Automate Processes. From email campaigns to content creation, they streamline production through AI automations. Mundane manual tasks become automated processes.
3. They Master Multi-Tool Ecosystems. They tap into a range of tools—automation platforms, data layers, and LLM APIs—to piece together custom marketing solutions that fit their GTM motion. 
4. They Deliver Substantial Productivity Output. Their ability to fuse AI with their marketing know-how unlocks a multiplier effect, propelling key metrics and organizational growth.

So, how did we get here?

AI and the Collapse of the Talent Stack

Our work is often defined by our goals and the tools we use to achieve them. For the past two decades, our talent stack and team structures have remained largely unchanged, including how we define roles and the lines between them.

For the most part, someone was considered a “unicorn” if they had skills and capabilities outside of their main area of expertise. Progressing in your field meant specializing, further solidifying the lines between roles and responsibilities.

But as AI capabilities continue to expand, traditional job roles will begin to blur. Product managers will casually code, performance marketers will design their own ads, and designers will step into copywriting—all powered by AI. What was once considered “unicorn” territory will become the new baseline.

In the coming years, it will be critical to think strategically and adapt quickly to emerging tech to thrive.

This shift isn’t limited to marketing; it will impact the entire talent stack. However, marketing functions are likely to be the most affected, as reflected in recent AI job impact reports and studies.

From Individual Contributor to Systems Orchestrator: AI Agents & Changing Team Structures

The classic SaaS martech stack we’ve relied on for so long is evolving as it’s challenged by the promise of AI agents. As these agents come to fruition, they’ll have a substantial impact on both team structures and roles of individual contributors. The role of the AI agent—and how marketers are using them—is going beyond automating tasks to impact how teams operate, scale, and deliver value.

In the past, traditional marketing roles focused on personal output: campaigns managed, content created, or leads generated. The AI-native marketer, however, can utilize AI agents to build and then orchestrate systems that multiply their contributions and impact beyond these simple outputs.

As these roles start to shift, it’s becoming abundantly clear that with the right AI-powered tools, anyone—across levels and functions—can push themselves to new heights as systems designers, managers, and orchestrators.

How This is Unfolding in Performance Marketing: AI-Native Pods

Ad buying is becoming increasingly streamlined as AI automates campaign management. Tasks that once took hours—like setting up campaigns, conducting keyword research, and configuring ad groups—are now faster and easier.

Today’s performance marketers are expanding their roles beyond managing and optimizing campaigns. They’re making creative decisions, producing ads, and setting up analytics and tracking systems.

What was once a unique, “unicorn” skill composition, often found in small teams or at fast-growing startups, is becoming the industry standard going forward. With this shift, we’re seeing silos and boundaries between roles break down and marketers developing AI-powered systems to fuel their workflows.

Content Generation Systems have already replaced individual content creation, with many marketers now designing content frameworks that auto-generate variations for different channels, adapt tone based on audience segments, and optimize based on performance data.

Teams will start to reshape, consolidate, and organize around these functional systems, forming function-oriented pods. A Demand Generation Pod, for example, would combine the capabilities and skill sets of automation engineers, content strategists, and analytics specialists. The Customer Journey Pod would merge product marketing, customer success, and technical integration, while the Brand Experience Pod would unite creative direction, content systems, and engagement automation.

Teams structured this way are true AI-native teams, powered by 10X AI marketers.

The Rise of 10X AI Marketers

Your CEO or founder likely had a bunch of AI initiatives in 2024. The marketer that showed the most curiosity, rushed to be there first, and ran the most experiments is likely your future 10X AI marketer.

Imagine a marketer fluent in AI tools, automation workflows, and data analytics—someone equally comfortable in strategic boardroom discussions and prompt-engineering brainstorms. They’re not intimidated by new, seemingly complex tech; they thrive on it. They run experiments with agents, memory modules, and knowledge bases to push campaigns further, faster.

Instead of treating AI like a mysterious black box or hesitating in the face of this major change, they see it as a partner, an extension of their own marketing expertise, and a way to refine their talents and ensure they stay relevant. Driven by curiosity, they constantly explore fresh applications, workflows, and prompts. Their end goal: drive business growth with measurable, AI-fueled outcomes.

The Core Skill Set of a 10X AI Marketer

• Strong Automation Know-How: These marketers are comfortable with workflow automation platforms like Make.com, N8N, or Zapier for managing data flow and automating anything from lead nurturing to social media scheduling.
• Coding or Rapid Prototyping Skills: Even basic coding literacy can be a massive advantage. Whether it’s tweaking an API or setting up a custom script, these marketers quickly turn ideas into action.
• Data Analytics Background: AI thrives on data, so understanding how to collect, analyze, and interpret it is crucial for guiding smarter decisions and measuring impact.
• Prompt Engineering Foundation: They know how to coax the best outcomes from large language models—whether via zero-shot, few-shot, or chain-of-thought prompting.
• Critical and Strategic Thinking: AI doesn’t replace strategic acumen. It amplifies it. 10X AI Marketers understand the marketing funnel end to end and apply AI where it matters most.
• Copywriting and Empathy: AI can generate text, but it can’t always sense the emotional undercurrents of your audience. Strong human copywriting skills ensure the final message resonates.
• Tech Savvy and Adaptable: With AI evolving daily, a willingness to learn, test, and pivot is essential. 10X AI Marketers are always on the hunt for next-gen tools and capabilities.
• Curiosity and Ongoing Learning: The AI frontier is still wide open. 10X AI Marketers embrace continuous learning—always refining workflows and exploring fresh solutions.

The Core Concepts 10X AI Marketers Must Master

1. Prompt Engineering

Prompt engineering is about speaking to an LLM in a way that elicits high-quality, relevant responses. The best AI Marketers know how to:

• Use Zero-Shot and Few-Shot Approaches: Specify the context, target audience, or writing style to get the best possible copy.
• Chain of Reasoning: Have the LLM walk through logical steps, improving the detail and clarity of the response.
• Iterate and Refine: They’re not satisfied with the first draft—they experiment, revise, and push the AI to produce sharper content.

2. Leveraging AI Agents and Agentic Workflows

LLMs can do more than answer questions—they can act as agents that handle tasks end to end. By wiring up an LLM to a database and knowledge system, you can create fully- or semi-automated workflows. For example, an AI agent might:

• Monitor incoming data, such as new leads or signups.
• Generate personalized follow-ups.
• Update CRM entries automatically based on user signals.

This is sometimes referred to as an “agentic workflow”—where software is built around an LLM brain with memory and a knowledge base. These intelligent processes can be custom built to meet specific needs, reach certain goals, or provide defined outputs. They can also learn and evolve based on new data, gradually increasing their sophistication and value to the business.

Common Tech-Stack for 10X AI Marketers

These are the commonly used tools in an AI marketer’s tech stack, but we see a lot of evolution and consolidation between these tools. Foundational LLMs are also becoming so much more integrated and versatile in use that you often don’t need a ton of tools for day-to-day execution.

Why Invest in 10X AI Marketers

• Velocity and Iteration: Traditional marketing campaigns can take weeks—or even months—from concept to execution. A 10X AI Marketer can rapidly test ideas, generate new angles, and optimize messaging in mere hours.
• Resource Efficiency: Entire marketing teams can shift to higher-level strategy and problem-solving when routine tasks are automated. This not only saves costs but elevates creativity across the board. After all, the cost of marketing talent and resources is part of our loaded CAC. 
• Data Informed Actions: By combining analytics with AI, they see patterns faster and act on them immediately. Instead of being stuck in endless data crunching, they let AI highlight anomalies, suggest next steps, or trigger proactive interventions.
• Hyper-Personalization at Scale: One of AI’s greatest strengths is personalization. 10X AI Marketers feed user data and context into LLMs to tailor messaging at the individual level, creating more meaningful customer experiences.
• Competitive Advantage: In a world where attention is scarce, being first to market with new AI-driven tactics can be the difference between leading the pack and playing catch-up. The 10X AI Marketer stays ahead of trends, making their brand impossible to ignore.

Final Thoughts

AI-led growth represents the new frontier of marketing. The AI shift and the current tech stack of agents and tools will enable the right marketers to become 10X versions of themselves—10X AI Marketers—who use available tech to push past traditional marketing and team limits to accelerate results. From prompt engineering prowess to agile automation design, their secret weapon lies in integrating newly accessible tech with strategic vision.

It’s about seeing AI not as a replacement for human creativity and strategy but as an amplifier of our capabilities. The most successful practitioners in this space are those who can bridge the gap between technical possibilities and business realities.

The 10X AI Marketer is more than a role—it’s a mindset. When embraced, you’ll find that AI-led growth isn’t a passing trend, it’s the path to becoming an unstoppable force in today’s hyper-competitive market.

The post AI-Led Growth: The Rise of the 10X Marketers and AI-Native Teams appeared first on NoGood™: Growth Marketing Agency.

As a result, Snapchat marketing and creative agencies are now in high demand. In this blog post, we’ll explore the top Snapchat marketing agencies for Snapchat paid ads, content creation, and influencer campaigns.

Our Best 14 Snapchat Marketing Agencies:

1. NoGood
2. VaynerMedia
3. Viral Nation
4. Ignite Social Media
5. Disruptive Advertising
6. inBeat Agency
7. Newbird
8. Online Optimism
9. AdvertiseMint
10. The Social Shepherd
11. MuteSix
12. Voy Media
13. Taktical Digital
14. The Snow Agency

1. NoGood

Description: At NoGood, we’re a dynamic growth and performance marketing agency in the heart of New York City. Our experience in effectively executing both paid and organic Snapchat campaigns runs deep. Our client roster includes esteemed names like Nike, ByteDance, JVN, and Intuit, as well as various startup ventures and ambitious scale-ups.

When you choose to partner with us, we assemble a customized growth team of established experts. We don’t just grasp the intricacies of your business – we’re fully committed to propelling rapid expansion that unlocks your complete potential.

Office Location: New York, NY (HQ)

Year Founded: 2017

Team Size: 50 – 100 employees

Key Services: Creator Studio, Social Ads, Content Marketing, Influencer Marketing, Video Marketing

Industries Served: SaaS, Healthcare, Fintech, B2B, Consumer, Crypto

Case Studies: View all case studies.

2. VaynerMedia

Description: VaynerMedia is a digital and creative agency that specializes in social media marketing, including Snapchat. They offer services such as strategy and planning, creative development, influencer marketing, and media buying. They’ve worked with brands like PepsiCo, Toyota, and Unilever to create engaging content for Snapchat. Their team of experts can help you create customized Snapchat filters, ads, and sponsored lenses that resonate with your target audience.

Office Location: New York, NY, with offices around the world

Year Founded: 2009

Team Size: 501 – 1,000 employees

Key Services: Social Media Management, Video Ads, Marketing Content, Brand Building, Video Production

Industries Served: N/A

Case Studies: N/A

3. Viral Nation 

Description: Viral Nation is a leading influencer marketing agency that has expanded its services to include Snapchat advertising campaigns. They specialize in creating engaging content for Snapchat, including filters, lenses, and sponsored posts. Their team of experts can help your brand reach a wider audience, generate leads, and drive sales through targeted Snapchat ads. Viral Nation also offers comprehensive analytics to track the performance of your campaigns. Their pricing is based on a commission structure.

Office Location: Mississauga, Ontario

Year Founded: 2014

Team Size: 201 – 500 employees

Key Services: Influencer Marketing, Personalized Content Creation, Social Media Marketing Services, Advertising Strategies, Community Management

Industries Served: Beverages, Gaming, Consumer

Case Studies: View all case studies.

4. Ignite Social Media 

Description: Ignite Social Media is a social media marketing agency that offers Snapchat advertising services. They specialize in creating visually stunning and engaging Snapchat content for brands, including sponsored stories, lenses, and filters. Ignite Social Media can help you reach new audiences and achieve your marketing goals through innovative Snapchat campaigns. Their pricing is based on a project basis.

Office Location: Cary, North Carolina

Year Founded: 2007

Team Size: 51 – 200 employees

Key Services: Social Media Platforms Marketing, Digital Advertising, Content Marketing, Influencer Marketing, Blogger Outreach, Community Management

Industries Served: Automotive, Food & Beverage, Consumer, Health & Wellness, Entertainment, Non-Profit

Case Studies: View all case studies.

5. Disruptive Advertising

Description: Disruptive Advertising leverages Snapchat’s ads to elevate brand awareness, encourage audience interest, and boost conversions for businesses. To ensure the suitability of Snapchat as a potent marketing channel for their clients, the agency undertakes a comprehensive audience assessment. The process entails scrutinizing demographic data, primarily obtained from the client’s Facebook, Instagram, and Google data insights. By doing so, they gain valuable insights into the age, gender, and lifestyle demographics of the client’s current audiences. This data is then directly compared to Snapchat’s demographics to gauge alignment and potential reach.

Office Location: Pleasant Grove, UT

Year Founded: 2012

Team Size: 51 – 200 employees

Key Services: Paid Search Advertising, Conversion Rate Optimization, Social Media Marketing Services, Display Advertising, Google Analytics, SEO, Lifecycle Marketing, Creative

Industries Served: eCommerce, Tech, Health & Wellness, Financial Services, Beauty, Home Services

Case Studies: View all case studies.

6. inBeat Agency

Description: inBeat is a premier digital marketing agency that specializes in Snapchat advertising. With a focus on empowering brands, they offer top-notch marketing campaigns to engage potential customers effectively. Leveraging Snapchat’s competitive CPM and audience-specific targeting capabilities, inBeat’s marketing team ensures maximum impact and helps businesses achieve their specific business goals.

As a dedicated Snapchat ads agency, inBeat takes pride in cultivating strong relationships with top creators. This enables them to produce outstanding user-generated content that stands out from the crowd and resonates with potential customers. Their expertise in crafting compelling marketing strategies ensures brands receive optimal visibility and engagement on the platform, aligning seamlessly with their unique business objectives.

Office Location: New York, NY, with offices around the world

Year Founded: 2018

Team Size: 51 – 200 employees

Key Services: Influencer Marketing, Social Media Management, Digital Marketing Agency, Marketing Content Creation

Industries Served: Consumer Packaged Goods, DTC, Healthcare, App Marketing

Case Studies: View all case studies.

7. Newbird

Description: Newbird is a top advertising agency that boasts a decade of unparalleled experience crafting captivating and successful Snapchat ad campaigns for a diverse clientele. With a remarkable track record of over 400 satisfied clients, Newbird’s profound knowledge of Snapchat advertising makes them the ultimate choice for businesses seeking to connect with a younger audience and attain their objectives through this dynamic platform.

Newbird’s mastery of Snapchat advertising shines through its comprehensive approach, placing utmost importance on grasping a client’s brand essence and understanding their unique customer journeys. This customer-centric strategy allows them to create tailored marketing materials that resonate deeply with the target audience, ensuring maximum impact on this vibrant advertising platform.

Office Location: Buffalo, New York

Year Founded: 2008

Team Size: 11 – 50 employees

Key Services: Website Design, Social Media Marketing Services, Mobile App Marketing, Internet Marketing, Branding, Video Production & Content Development, SEO, PPC, Paid Media Advertising, Email Marketing

Industries Served: Beauty, Health, Clothing

Case Studies: View all case studies.

8. Online Optimism

Description: Online Optimism is a leading ad agency proficient in assisting businesses with effectively reaching their target audience on the immensely popular social media platform. With their Snapchat Ads Certified social media team, Online Optimism remains updated on the latest social media strategies, guaranteeing optimal outcomes for their esteemed clients.

Functioning as a seasoned Snapchat advertising agency, Online Optimism designs ad campaigns that strategically target audiences across various stages of the sales funnel. Collaborating closely with their clients, they adeptly set ad budgets and pinpoint ideal audience personas, ensuring exceptional return on investment (ROI) for each campaign, even on a tight budget.

Office Location: New Orleans, LA

Year Founded: 2012

Team Size: 11 – 50 employees

Key Services: Website Design, Search Engine Optimization, Social Media Management, Digital Advertising, Content Marketing, Branding 

Industries Served: Hospitality, Healthcare, Automotive, Legal, Education, Government, Non-Profit, Cybersecurity, Consumer

Case Studies: View all case studies.

9. AdvertiseMint

Description: AdvertiseMint is a digital advertising agency specializing in aiding prosperous companies with advertising effectively on Snapchat. Their extensive knowledge of Snapchat advertising coupled with a strong emphasis on cost-effectiveness and user-friendly approaches positions them as a dependable option for businesses seeking to optimize their presence on the platform.

At AdvertiseMint, a team of Snapchat experts wholeheartedly dedicates themselves to curating captivating and customized ad campaigns tailored to meet each client’s unique needs. Their commitment to delivering engaging and relevant content ensures businesses receive the best possible results from their Snapchat advertising endeavors.

Office Location: Los Angeles, CA

Year Founded: 2014

Team Size: 11 – 50 employees

Key Services: Social Advertising, Instagram Advertising, Facebook Advertising, Digital Advertising

Industries Served: N/A

Case Studies: N/A

10. The Social Shepherd

Description: The Social Shepherd is made up of renowned experts in customer acquisition and marketing campaign scaling for their esteemed clients. With a wealth of experience in effectively managing Snapchat Ads, complemented by their exceptional creative abilities, The Social Shepherd guarantees outstanding outcomes for all campaign endeavors.

Having a solid track record of delivering remarkable results, The Social Shepherd boasts an impressive 5.4x return on ad spend (ROAS) for prominent clients like the UK’s Love Leggings. This success story is a testament to their proficiency and ability to deliver exceptional results for businesses they partner with.

Office Location: New York, NY, with offices in the UK

Year Founded: 2018

Team Size: 50 – 200 employees

Key Services: Social Media Marketing, Paid Social, Video Production, Influencer Marketing, eCommerce Marketing, Social Media Content

Industries Served: Skincare, Food & Beverage, Consumer, Jewelry, Clothing

Case Studies: View all case studies.

11. MuteSix

Description: MuteSix boasts a team of expert strategists and creatives well-versed in streamlining the production of captivating Snapchat ads that instantly capture attention. Their diverse ad formats, including Single Image, Video, Lenses, and Story Ads, are thoughtfully optimized to engage customers effectively throughout their journey.

Through extensive A/B testing, MuteSix uncovers the most successful elements of their Snapchat-specific content to thrive on this multimedia platform. This invaluable insight fuels their ability to consistently deliver successful campaigns and drive impressive client results.

Office Location: Culver City, California

Year Founded: 2014

Team Size: 201 – 500 employees

Key Services: Paid Social, Paid Search, Email Marketing, Amazon Marketing, SMS Marketing, Content Creation, Lifecycle Marketing

Industries Served: Home Goods, Consumer, Food & Beverage, App Marketing

Case Studies: View all case studies.

12. Voy Media

Description: Voy Media brings a wealth of experience, catering to an extensive clientele spanning Fortune 500 companies to burgeoning start-ups. Their success stories include helping businesses achieve substantial sales growth and establishing a proven track record of excellence.

The team’s expertise lies in crafting captivating images that effectively halt scrolling and copy that captivates readers. Doing so safeguards their clients’ marketing efforts from disappearing into the digital abyss, ensuring maximum visibility and engagement for their brand messages.

Office Location: New York, NY

Year Founded: 2016

Team Size: 11 – 50 employees

Key Services: Facebook Ads, Mobile Attribution, Lifecycle Marketing, Mobile App Marketing, Digital Advertising

Industries Served: Gaming, Beauty, App Marketing, Education, Fashion, Food & Beverage, Health & Wellness

Case Studies: View all case studies.

13. Taktical Digital

Description: Taktical Digital is an award-winning advertising agency with a team of data-driven specialists that are dedicated to Snapchat advertising. Their advertising campaigns help raise awareness for your brand on this dynamic social media platform.

Taktical Digital recognizes that storytelling is at the heart of advertising on Snapchat, and they work to present the brands they work with through compelling narratives and eye-catching visuals. They offer multiple Snapchat ad formats and employ an in-house creative team, so you can be confident in every aspect of your campaign.

Office Location: New York, NY

Year Founded: 2013

Team Size: 51 – 200 employees

Key Services: SEO, Paid Social, Paid Search, Landing Page Optimization, Content Marketing, eCommerce Advertising

Industries Served: Home Goods, Clothing, Jewelry, Footwear

Case Studies: View all case studies.

14. The Snow Agency

Description: The Snow Agency is an award-winning social media and influencer marketing agency that strives to bring cutting-edge advertising solutions to their clients to connect brands with their target audiences. They boast expertise across a wide variety of social media platforms, including Snapchat, and they work closely with direct-to-consumer brands to launch and scale their advertising efforts.

They take a clearly outlined approach to Snapchat advertising, beginning with analyzing your objectives and goals. Then, they determine who exactly they want to target, and from there, they launch your campaign and monitor it closely to ensure it’s performing as expected and driving the results you deserve.

Office Location: Miami, Florida

Year Founded: N/A

Team Size: 51 – 200 employees

Key Services: Paid Social Ads, UGC Content Marketing, SEO, CRO, Web Design, Email & SMS Marketing

Industries Served: Beauty & Cosmetics, Home Goods, Clothing, Health & Wellness, Sports

Case Studies: View all case studies.

Frequently Asked Questions

Push the Boundaries of Your Advertising on Snapchat

Snapchat has become an essential platform for brands looking to engage with their target audience in a creative and impactful way. These top Snapchat marketing agencies offer a range of services, including paid ads, content creation, and influencer campaigns. 

Each agency has its unique strengths, so it’s essential to carefully evaluate their offerings and expertise to find the perfect partner for your brand. Armed with this information, you can confidently choose the right agency to help your brand succeed on Snapchat.

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