[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-how-ai-visibility-platforms-help-brands-win-ai-generated-recommendations-en":3,"ArticleBody_3LSltZRVanzo3Uiw801eoqvQM7FwuFIVAvKI0kQRs":130},{"article":4,"relatedArticles":122,"locale":25},{"id":5,"title":6,"slug":7,"content":8,"htmlContent":9,"excerpt":10,"category":11,"tags":12,"metaDescription":10,"wordCount":13,"readingTime":14,"publishedAt":15,"sources":16,"sourceCoverage":17,"transparency":19,"seo":22,"language":25,"featuredImage":26,"featuredImageCredit":27,"isFreeGeneration":31,"niche":32,"geoTakeaways":36,"geoFaq":45,"entities":55},"69dca7dafdc9d5811a337adc","How AI Visibility Platforms Help Brands Win AI-Generated Recommendations","how-ai-visibility-platforms-help-brands-win-ai-generated-recommendations","AI assistants and generative search are becoming the new front page of the internet.  \nPeople ask, “What’s the best X for me?” and expect a single, confident answer.\n\nIn that world, your brand either appears in the AI’s shortlist—or it disappears.  \nAI visibility platforms help ensure you show up in that answer set, accurately and competitively.\n\n💡 **Key takeaway:** AI visibility is emerging alongside SEO, not replacing it.\n\n---\n\n## 1. Why AI visibility is the new SEO for brands\n\nAI visibility platforms make brands more discoverable and correctly described in AI assistants, recommendation engines, and generative search. They sit next to your SEO and adtech stack, structuring brand data so [large language models](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLarge_language_model) can confidently surface you in responses.\n\nThe battlefield has shifted from many blue links to a single AI-generated paragraph or shortlist. As users move from keyword searches to conversational queries, visibility becomes highly “winner-takes-most.”\n\n📊 **Fastest-growing discovery use cases:**  \n- “What tools should I use to…?”  \n- “Which provider is best for a company like mine?”  \n- “What’s the safest option for…?”\n\nThe challenge:  \n- Brand data is scattered across sites, CRMs, feeds, app stores, and reviews  \n- AI systems need structured, consistent, trustworthy signals\n\nAI visibility platforms close this gap by:  \n- Consolidating and normalizing brand data  \n- Publishing it in formats and channels AI models can ingest and verify  \n\nThis boosts the odds you’re chosen when models assemble recommendations.\n\n⚠️ **Strategic stakes for [marketing and growth leaders](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FMarketing_strategy):**  \n- Loss of brand recall as generic AI answers replace brand-led journeys  \n- Dependence on opaque recommendation logic  \n- No transparent way to influence how you’re described and ranked  \n\nAI visibility turns this into a proactive discipline, giving you more control over how your brand appears inside AI responses.\n\n---\n\n## 2. How AI visibility platforms optimize brands for AI-generated recommendations\n\nThe goal is a unified, machine-readable understanding of your brand that AI systems can trust.\n\n**Core capabilities:**  \n- Ingest data from websites, product catalogs, support logs, reviews, marketplaces, and social profiles  \n- Build a unified brand graph enriched with:  \n  - Who you serve (segments, geos, industries)  \n  - What you offer (products, packages, tiers)  \n  - Why you win (benefits, proof points, differentiators)\n\nThis structure helps AI systems understand, compare, and cite your brand in context.\n\nPlatforms also generate machine-friendly assets:  \n- Structured data and schema markup  \n- Knowledge-panel-style brand profiles  \n- Product, service, and FAQ objects for search engines and LLMs  \n\n💡 **Example:** Clear taxonomies and attributes help AI answer queries like “best budget-friendly B2B payment platform for SMBs” with brands that explicitly match those descriptors.\n\n**Trust and authority signals:**  \n- Certifications and security attestations  \n- Partner lists and verified reviews  \n- Resolution of contradictions across channels  \n\nThis improves model confidence, especially in regulated or high-risk categories.\n\nAdvanced platforms add an “AI SERP analytics” layer so teams can see:  \n- Where and how they appear in AI-generated answers  \n- Share of presence vs. competitors  \n- Sentiment of cited content and coverage gaps  \n\n💼 **Integration points:** AI visibility platforms often:  \n- Consume segments from [CDPs](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCDPS)  \n- Feed enriched metadata to SEO\u002Fon-site search tools  \n- Provide recommendation-ready profiles to marketplaces and AI partners  \n\nThey become an orchestration layer keeping brand data consistent across human-facing and AI-facing touchpoints.\n\n---\n\n## 3. Practical playbook: getting your brand AI-visible now\n\n**Start with an [AI visibility audit](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FWebsite_audit):**  \n- Map where your brand appears in AI surfaces (search overviews, shopping assistants, chatbots)  \n- Compare those appearances with your official positioning:  \n  - Are prices, categories, and features accurate?  \n  - Are differentiators and use cases visible?  \n  - Which sources is the AI relying on?\n\n⚡ **Action step:** Use this audit to prioritize which data sources and profiles must be cleaned and centralized first.\n\n**Build a canonical brand knowledge layer:**  \n- Create a single source of truth for:  \n  - Core facts (markets, product lines, pricing model, compliance)  \n  - Key narratives (value propositions, proof points, positioning)  \n- Ensure it is:  \n  - Structured and machine-readable  \n  - Version-controlled with clear ownership  \n  - Connected so updates propagate to AI-relevant channels\n\n**Define measurement and experimentation:**  \n- Track KPIs such as:  \n  - Share of AI recommendations for priority queries  \n  - Inclusion in top AI summaries for your category  \n  - Uplift in assisted conversions from AI-origin journeys  \n- Run experiments by adjusting use-case clarity, category labels, and proof points, then measure visibility and quality of mentions.\n\n⚠️ **Governance and ethics:**  \n- Avoid overstated or misleading claims  \n- Keep compliance and risk-related content current  \n- Be transparent about data sources and endorsements  \n\nAI visibility should enhance trust and reduce [hallucinations](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FHallucination), not game algorithms.\n\n---\n\nAI visibility platforms are becoming as foundational as SEO and analytics for brands that want to be discovered, trusted, and recommended in an AI-first world. They unify and structure brand data, strengthen trust signals, and reveal how AI systems perceive and surface your business.\n\nAssess your AI footprint, run an AI visibility audit, and explore platforms that can centralize your brand knowledge. Align marketing, data, and product leaders around a shared roadmap so that when an AI is asked, “Which solution should I choose?”, your brand is the most accurate, compelling answer.","\u003Cp>AI assistants and generative search are becoming the new front page of the internet.\u003Cbr>\nPeople ask, “What’s the best X for me?” and expect a single, confident answer.\u003C\u002Fp>\n\u003Cp>In that world, your brand either appears in the AI’s shortlist—or it disappears.\u003Cbr>\nAI visibility platforms help ensure you show up in that answer set, accurately and competitively.\u003C\u002Fp>\n\u003Cp>💡 \u003Cstrong>Key takeaway:\u003C\u002Fstrong> AI visibility is emerging alongside SEO, not replacing it.\u003C\u002Fp>\n\u003Chr>\n\u003Ch2>1. Why AI visibility is the new SEO for brands\u003C\u002Fh2>\n\u003Cp>AI visibility platforms make brands more discoverable and correctly described in AI assistants, recommendation engines, and generative search. They sit next to your SEO and adtech stack, structuring brand data so \u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLarge_language_model\" class=\"wiki-link\" target=\"_blank\" rel=\"noopener\">large language models\u003C\u002Fa> can confidently surface you in responses.\u003C\u002Fp>\n\u003Cp>The battlefield has shifted from many blue links to a single AI-generated paragraph or shortlist. As users move from keyword searches to conversational queries, visibility becomes highly “winner-takes-most.”\u003C\u002Fp>\n\u003Cp>📊 \u003Cstrong>Fastest-growing discovery use cases:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>“What tools should I use to…?”\u003C\u002Fli>\n\u003Cli>“Which provider is best for a company like mine?”\u003C\u002Fli>\n\u003Cli>“What’s the safest option for…?”\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>The challenge:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Brand data is scattered across sites, CRMs, feeds, app stores, and reviews\u003C\u002Fli>\n\u003Cli>AI systems need structured, consistent, trustworthy signals\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>AI visibility platforms close this gap by:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Consolidating and normalizing brand data\u003C\u002Fli>\n\u003Cli>Publishing it in formats and channels AI models can ingest and verify\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>This boosts the odds you’re chosen when models assemble recommendations.\u003C\u002Fp>\n\u003Cp>⚠️ \u003Cstrong>Strategic stakes for \u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FMarketing_strategy\" class=\"wiki-link\" target=\"_blank\" rel=\"noopener\">marketing and growth leaders\u003C\u002Fa>:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Loss of brand recall as generic AI answers replace brand-led journeys\u003C\u002Fli>\n\u003Cli>Dependence on opaque recommendation logic\u003C\u002Fli>\n\u003Cli>No transparent way to influence how you’re described and ranked\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>AI visibility turns this into a proactive discipline, giving you more control over how your brand appears inside AI responses.\u003C\u002Fp>\n\u003Chr>\n\u003Ch2>2. How AI visibility platforms optimize brands for AI-generated recommendations\u003C\u002Fh2>\n\u003Cp>The goal is a unified, machine-readable understanding of your brand that AI systems can trust.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Core capabilities:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Ingest data from websites, product catalogs, support logs, reviews, marketplaces, and social profiles\u003C\u002Fli>\n\u003Cli>Build a unified brand graph enriched with:\n\u003Cul>\n\u003Cli>Who you serve (segments, geos, industries)\u003C\u002Fli>\n\u003Cli>What you offer (products, packages, tiers)\u003C\u002Fli>\n\u003Cli>Why you win (benefits, proof points, differentiators)\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>This structure helps AI systems understand, compare, and cite your brand in context.\u003C\u002Fp>\n\u003Cp>Platforms also generate machine-friendly assets:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Structured data and schema markup\u003C\u002Fli>\n\u003Cli>Knowledge-panel-style brand profiles\u003C\u002Fli>\n\u003Cli>Product, service, and FAQ objects for search engines and LLMs\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>💡 \u003Cstrong>Example:\u003C\u002Fstrong> Clear taxonomies and attributes help AI answer queries like “best budget-friendly B2B payment platform for SMBs” with brands that explicitly match those descriptors.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Trust and authority signals:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Certifications and security attestations\u003C\u002Fli>\n\u003Cli>Partner lists and verified reviews\u003C\u002Fli>\n\u003Cli>Resolution of contradictions across channels\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>This improves model confidence, especially in regulated or high-risk categories.\u003C\u002Fp>\n\u003Cp>Advanced platforms add an “AI SERP analytics” layer so teams can see:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Where and how they appear in AI-generated answers\u003C\u002Fli>\n\u003Cli>Share of presence vs. competitors\u003C\u002Fli>\n\u003Cli>Sentiment of cited content and coverage gaps\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>💼 \u003Cstrong>Integration points:\u003C\u002Fstrong> AI visibility platforms often:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Consume segments from \u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCDPS\" class=\"wiki-link\" target=\"_blank\" rel=\"noopener\">CDPs\u003C\u002Fa>\u003C\u002Fli>\n\u003Cli>Feed enriched metadata to SEO\u002Fon-site search tools\u003C\u002Fli>\n\u003Cli>Provide recommendation-ready profiles to marketplaces and AI partners\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>They become an orchestration layer keeping brand data consistent across human-facing and AI-facing touchpoints.\u003C\u002Fp>\n\u003Chr>\n\u003Ch2>3. Practical playbook: getting your brand AI-visible now\u003C\u002Fh2>\n\u003Cp>\u003Cstrong>Start with an \u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FWebsite_audit\" class=\"wiki-link\" target=\"_blank\" rel=\"noopener\">AI visibility audit\u003C\u002Fa>:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Map where your brand appears in AI surfaces (search overviews, shopping assistants, chatbots)\u003C\u002Fli>\n\u003Cli>Compare those appearances with your official positioning:\n\u003Cul>\n\u003Cli>Are prices, categories, and features accurate?\u003C\u002Fli>\n\u003Cli>Are differentiators and use cases visible?\u003C\u002Fli>\n\u003Cli>Which sources is the AI relying on?\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>⚡ \u003Cstrong>Action step:\u003C\u002Fstrong> Use this audit to prioritize which data sources and profiles must be cleaned and centralized first.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Build a canonical brand knowledge layer:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Create a single source of truth for:\n\u003Cul>\n\u003Cli>Core facts (markets, product lines, pricing model, compliance)\u003C\u002Fli>\n\u003Cli>Key narratives (value propositions, proof points, positioning)\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fli>\n\u003Cli>Ensure it is:\n\u003Cul>\n\u003Cli>Structured and machine-readable\u003C\u002Fli>\n\u003Cli>Version-controlled with clear ownership\u003C\u002Fli>\n\u003Cli>Connected so updates propagate to AI-relevant channels\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cstrong>Define measurement and experimentation:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Track KPIs such as:\n\u003Cul>\n\u003Cli>Share of AI recommendations for priority queries\u003C\u002Fli>\n\u003Cli>Inclusion in top AI summaries for your category\u003C\u002Fli>\n\u003Cli>Uplift in assisted conversions from AI-origin journeys\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fli>\n\u003Cli>Run experiments by adjusting use-case clarity, category labels, and proof points, then measure visibility and quality of mentions.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>⚠️ \u003Cstrong>Governance and ethics:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Avoid overstated or misleading claims\u003C\u002Fli>\n\u003Cli>Keep compliance and risk-related content current\u003C\u002Fli>\n\u003Cli>Be transparent about data sources and endorsements\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>AI visibility should enhance trust and reduce \u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FHallucination\" class=\"wiki-link\" target=\"_blank\" rel=\"noopener\">hallucinations\u003C\u002Fa>, not game algorithms.\u003C\u002Fp>\n\u003Chr>\n\u003Cp>AI visibility platforms are becoming as foundational as SEO and analytics for brands that want to be discovered, trusted, and recommended in an AI-first world. They unify and structure brand data, strengthen trust signals, and reveal how AI systems perceive and surface your business.\u003C\u002Fp>\n\u003Cp>Assess your AI footprint, run an AI visibility audit, and explore platforms that can centralize your brand knowledge. Align marketing, data, and product leaders around a shared roadmap so that when an AI is asked, “Which solution should I choose?”, your brand is the most accurate, compelling answer.\u003C\u002Fp>\n","AI assistants and generative search are becoming the new front page of the internet.  \nPeople ask, “What’s the best X for me?” and expect a single, confident answer.\n\nIn that world, your brand either...","trend-radar",[],851,4,"2026-04-13T08:24:17.595Z",[],{"totalSources":18},0,{"generationDuration":20,"kbQueriesCount":18,"confidenceScore":21,"sourcesCount":18},47301,60,{"metaTitle":23,"metaDescription":24},"AI visibility platforms: 7 ways to win AI search now","AI assistants are the new search front page. Learn how AI visibility platforms optimize brands for AI-generated recommendations and future-proof your reach.","en","https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1611162617213-7d7a39e9b1d7?ixid=M3w4OTczNDl8MHwxfHNlYXJjaHwxfHx2aXNpYmlsaXR5JTIwcGxhdGZvcm1zJTIwb3B0aW1pemluZyUyMGJyYW5kc3xlbnwxfDB8fHwxNzc2MDY4NTcwfDA&ixlib=rb-4.1.0&w=1200&h=630&fit=crop&crop=entropy&auto=format,compress&q=60",{"photographerName":28,"photographerUrl":29,"unsplashUrl":30},"Alexander Shatov","https:\u002F\u002Funsplash.com\u002F@alexbemore?utm_source=coreprose&utm_medium=referral","https:\u002F\u002Funsplash.com\u002Fphotos\u002Fblue-red-and-green-letters-illustration-mr4JG4SYOF8?utm_source=coreprose&utm_medium=referral",true,{"key":33,"name":34,"nameEn":35},"marketing","Marketing Digital","Digital Marketing",[37,39,41,43],{"text":38},"AI visibility platforms consolidate and normalize brand data from 3–10 disparate sources (websites, CRMs, reviews, app stores) so brands are more likely to appear in single-answer AI recommendations.",{"text":40},"They produce machine-readable assets (schema, knowledge profiles, product\u002FFAQ objects) that increase an AI model’s confidence and citation likelihood, improving share of AI recommendations for priority queries.",{"text":42},"Trackable KPIs include share of AI recommendations, inclusion in top AI summaries, and uplift in assisted conversions; teams should run experiments and measure visibility changes over weeks to months.",{"text":44},"Treat AI visibility as a companion to SEO: it focuses on structured brand truth and trust signals (certifications, verified reviews, partner lists) that reduce hallucinations and regulatory risk.",[46,49,52],{"question":47,"answer":48},"What exactly does an AI visibility platform do for a brand?","An AI visibility platform centralizes, normalizes, and publishes structured brand data so AI systems can accurately discover, compare, and cite your business. In practice it ingests inputs from websites, product catalogs, support logs, reviews, marketplaces, and social profiles, builds a unified brand graph (who you serve, what you offer, why you win), and generates machine-friendly outputs like schema markup and knowledge-panel-style profiles. It also resolves contradictions, attaches trust signals (certifications, verified reviews), and provides analytics—such as share of presence and sentiment—so teams can see how often and why an AI recommends them versus competitors.",{"question":50,"answer":51},"How do I run an effective AI visibility audit?","Start by mapping where your brand currently appears across AI surfaces and comparing those results against your canonical positioning. First, collect examples of AI-generated answers, chat summaries, and shopping assistant recommendations for priority queries; then trace which sources the AI cited and note inaccuracies in price, category, or differentiators. Prioritize remediation based on traffic and conversion impact, clean and centralize the highest-impact data sources, and create a version-controlled canonical brand layer that propagates to AI-relevant channels. Finish by defining KPIs (share of AI recommendations, inclusion in top summaries, assisted conversions) and schedule iterative tests to measure improvements.",{"question":53,"answer":54},"How is AI visibility different from traditional SEO?","AI visibility complements rather than replaces SEO by focusing on structured, machine-readable brand truth and trust signals aimed at generative answers, not just organic ranking. While SEO optimizes content and links to rank in keyword-driven blue-link results, AI visibility ensures that factual attributes (segments, product tiers, pricing model), clear use-case labels, and authority signals (certifications, verified reviews, partner lists) are available in formats LLMs and recommendation engines can ingest and verify. The outcome is higher likelihood of inclusion in single-answer recommendations and reduced risk of being misrepresented or omitted by opaque AI ranking logic.",[56,62,66,70,75,80,85,89,93,97,102,105,110,114,117],{"id":57,"name":58,"type":59,"confidence":60,"wikipediaUrl":61},"69dca864dc9b12943743c770","AI assistants","concept",0.95,null,{"id":63,"name":64,"type":59,"confidence":65,"wikipediaUrl":61},"69dca865dc9b12943743c779","recommendation engines",0.9,{"id":67,"name":68,"type":59,"confidence":69,"wikipediaUrl":61},"69dca866dc9b12943743c77f","verified reviews",0.87,{"id":71,"name":72,"type":59,"confidence":73,"wikipediaUrl":74},"69dca866dc9b12943743c77e","structured data and schema markup",0.92,"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FSchema.org",{"id":76,"name":77,"type":59,"confidence":78,"wikipediaUrl":79},"69dca865dc9b12943743c775","large language models",0.97,"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLarge_language_model",{"id":81,"name":82,"type":59,"confidence":83,"wikipediaUrl":84},"69dca867dc9b12943743c782","hallucinations",0.85,"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FHallucination",{"id":86,"name":87,"type":59,"confidence":88,"wikipediaUrl":61},"69dca865dc9b12943743c773","SEO",0.98,{"id":90,"name":91,"type":59,"confidence":92,"wikipediaUrl":61},"69dca866dc9b12943743c781","B2B payment platform for SMBs",0.75,{"id":94,"name":95,"type":59,"confidence":96,"wikipediaUrl":61},"69dca865dc9b12943743c774","adtech stack",0.88,{"id":98,"name":99,"type":59,"confidence":100,"wikipediaUrl":101},"69dca866dc9b12943743c780","certifications and security attestations",0.86,"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCertification",{"id":103,"name":104,"type":59,"confidence":60,"wikipediaUrl":61},"69dca864dc9b12943743c771","generative search",{"id":106,"name":107,"type":108,"confidence":65,"wikipediaUrl":109},"69dca866dc9b12943743c77c","AI visibility audit","event","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FWebsite_audit",{"id":111,"name":112,"type":113,"confidence":65,"wikipediaUrl":61},"69dca865dc9b12943743c776","brand graph","other",{"id":115,"name":116,"type":113,"confidence":65,"wikipediaUrl":61},"69dca865dc9b12943743c778","marketplaces",{"id":118,"name":119,"type":120,"confidence":96,"wikipediaUrl":121},"69dca866dc9b12943743c77b","marketing and growth leaders","person","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FMarketing_strategy",[123],{"id":124,"title":125,"slug":126,"excerpt":127,"category":11,"featuredImage":128,"publishedAt":129},"69dc003d6704171d6b3e7e33","How to Market Security Live Demos at Digital Marketing Europe 2026","how-to-market-security-live-demos-at-digital-marketing-europe-2026","Digital marketers in 2026 operate where growth, data, and risk collide.  \nThird-party cookies are fading, first-party data is rising, and privacy laws keep tightening.  \nA single security slip can cor...","https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1648554090883-d23dbcf203a1?ixid=M3w4OTczNDl8MHwxfHNlYXJjaHwxfHxtYXJrZXRpbmclMjBzZWN1cml0eSUyMGxpdmUlMjBkZW1vc3xlbnwxfDB8fHwxNzc2MDI1NjYxfDA&ixlib=rb-4.1.0&w=1200&h=630&fit=crop&crop=entropy&auto=format,compress&q=60","2026-04-12T20:28:52.743Z",["Island",131],{"key":132,"params":133,"result":135},"ArticleBody_3LSltZRVanzo3Uiw801eoqvQM7FwuFIVAvKI0kQRs",{"props":134},"{\"articleId\":\"69dca7dafdc9d5811a337adc\"}",{"head":136},{}]