@prefix : . @prefix rdf: . @prefix rdfs: . @prefix owl: . @prefix xsd: . @prefix skos: . @prefix schema: . @prefix dct: . @prefix foaf: . @prefix prov: . : a owl:Ontology ; schema:name "What Is an Audience Interest Graph? — KG Ontology"@en ; schema:description "Semantic model of Abhi Yadav's article arguing that the customer file has stopped working as the operating asset for marketing — replaced by a living audience interest graph that senses, connects, decays, recommends, and learns. Covers four audience speeds, five graph functions, and the agentic commerce imperative."@en ; rdfs:label "What Is an Audience Interest Graph? — Knowledge Graph" ; schema:identifier "https://abhiyadav.substack.com/p/what-is-an-audience-interest-graph/" ; dct:source ; dct:creator :abhiYadav ; dct:date "2026-04-26"^^xsd:date . :AudienceSpeed a owl:Class ; rdfs:label "Audience Speed" ; rdfs:comment "One of four temporal layers at which audiences change — Identity (decade), Beliefs/Values (year), Interests (week-to-month), and Surfaces (fastest — where they show up today)." ; rdfs:isDefinedBy : . :GraphFunction a owl:Class ; rdfs:label "Graph Function" ; rdfs:comment "One of five required capabilities of a real audience interest graph — Sense, Connect, Decay, Recommend, Learn." ; rdfs:isDefinedBy : . :KeyPrinciple a owl:Class ; rdfs:label "Key Principle" ; rdfs:comment "A structural insight from the article about audience modeling, interest graphs, and the shift from static customer files to living relationship models." ; rdfs:isDefinedBy : . :article a schema:Article, schema:TechArticle ; rdfs:label "What Is an Audience Interest Graph and why your customer file is not enough" ; schema:name "What Is an Audience Interest Graph and why your customer file is not enough"@en ; schema:headline "What Is an Audience Interest Graph and why your customer file is not enough"@en ; schema:description "Abhi Yadav argues that the customer file — recording who bought what — has stopped working as marketing's operating asset. An audience interest graph replaces it: a living model of who the audience is, what they care about now, and where those interests surface. Five functions (Sense, Connect, Decay, Recommend, Learn) and four audience speeds define the architecture. Agentic commerce makes this urgent: brands without persistent preference become a SKU on a shelf the agent controls."@en ; schema:url ; schema:datePublished "2026-04-26"^^xsd:date ; schema:author :abhiYadav ; schema:publisher :dataToDecision ; schema:about :audienceInterestGraph, :customerFileLimits, :agenticCommerce, :interestVsIntent ; schema:hasPart :problemSection, :speedsSection, :graphSection, :agenticCommerceSection, :howToStartSection, :faqPage, :glossarySet, :howtoSection, :speedsEntities, :functionsSection, :principlesSection, : ; prov:wasGeneratedBy :kgGeneratorSkill . :abhiYadav a foaf:Person, schema:Person ; schema:name "Abhi Yadav"@en ; schema:url ; schema:identifier "https://substack.com/@abhiyadav" ; rdfs:comment "Author of the Data to Decision Activation newsletter on Substack. Writes about audience strategy, marketing technology, and the shift from customer files to interest graphs."@en . :dataToDecision a schema:PublicationIssue ; schema:name "Data to Decision Activation"@en ; schema:url . :audienceInterestGraph a skos:Concept, schema:DefinedTerm ; schema:name "Audience Interest Graph"@en ; schema:description "A living model of who the audience is, what they care about right now, and where those interests surface. Unlike a CDP (which organizes customer records), an interest graph organizes changing relationships. Five required functions: Sense, Connect, Decay, Recommend, Learn. If it does not recommend, it is analytics. If it does not learn, it is a dashboard. If it does not decay, it is already stale."@en ; rdfs:isDefinedBy : . :customerFileLimits a skos:Concept, schema:DefinedTerm ; schema:name "The Customer File Limit"@en ; schema:description "A customer file records who bought what — past transactions. It has stopped working as the operating asset for three reasons: attention shattered across surfaces, transactional loyalty stopped moving the needle, and privacy changes raised the cost of third-party data. The customer file depreciates; the interest graph compounds."@en ; rdfs:isDefinedBy : . :agenticCommerce a skos:Concept, schema:DefinedTerm ; schema:name "Agentic Commerce"@en ; schema:description "AI agents that browse, compare, recommend, and buy on behalf of consumers. Brands that lack persistent preference when the agent arrives become 'a SKU on a shelf the agent controls.' Agents do not manufacture trust — the relationship must exist beforehand."@en ; rdfs:isDefinedBy : . :interestVsIntent a skos:Concept, schema:DefinedTerm ; schema:name "Interest vs. Intent"@en ; schema:description "Intent says someone may be shopping now — it expires at checkout. Interest says this topic matters to them — it compounds over time. Scoring interest rather than just intent is a core function of the graph."@en ; rdfs:isDefinedBy : . :speedsEntities a schema:ArticleSection ; schema:name "Four Speeds of Audience Movement"@en ; schema:position 3 ; schema:hasPart :identitySpeed, :beliefsSpeed, :interestsSpeed, :surfacesSpeed . :identitySpeed a :AudienceSpeed ; rdfs:label "Identity (Decade-Scale)" ; rdfs:comment "The slowest layer — who the person fundamentally is. Changes at the pace of life stages: career shifts, family formation, relocation."@en . :beliefsSpeed a :AudienceSpeed ; rdfs:label "Beliefs & Values (Year-Scale)" ; rdfs:comment "What the person stands for — values, worldview, brand affinities. Changes at the pace of cultural shifts and personal growth."@en . :interestsSpeed a :AudienceSpeed ; rdfs:label "Interests (Week-to-Month)" ; rdfs:comment "What the person cares about right now — emerging hobbies, current preoccupations, active curiosities. This is where relevance lives. Example: pickleball went from niche to mainstream in roughly eighteen months."@en . :surfacesSpeed a :AudienceSpeed ; rdfs:label "Surfaces (Fastest)" ; rdfs:comment "Where the person shows up today — TikTok last year, Discord this quarter. The fastest-changing layer. Surface migration is a signal, not noise."@en . :functionsSection a schema:ArticleSection ; schema:name "Five Functions of a Real Interest Graph"@en ; schema:position 5 ; schema:hasPart :senseFn, :connectFn, :decayFn, :recommendFn, :learnFn . :senseFn a :GraphFunction ; rdfs:label "Sense" ; rdfs:comment "Detect emerging interests from first-party signals — site behavior, support transcripts, return reasons, search queries, content engagement."@en . :connectFn a :GraphFunction ; rdfs:label "Connect" ; rdfs:comment "Link entities — people, topics, products, surfaces, creators, communities — into a unified graph. The paid, owned, and earned brains must share memory."@en . :decayFn a :GraphFunction ; rdfs:label "Decay" ; rdfs:comment "Old signals must fade. An interest that was relevant six months ago may be noise today. Without decay, the graph becomes stale — 'if it does not decay, it is already stale.'"@en . :recommendFn a :GraphFunction ; rdfs:label "Recommend" ; rdfs:comment "Produce actionable output — which audience segment, which topic, which surface, which message. Without recommendation, the graph is analytics, not an operating asset."@en . :learnFn a :GraphFunction ; rdfs:label "Learn" ; rdfs:comment "Capture decision traces — records of what was decided, why, and whether it worked. Each decision sharpens the next. Without learning, the graph is a dashboard."@en . :principlesSection a schema:ArticleSection ; schema:name "Key Principles"@en ; schema:position 6 ; schema:hasPart :principle1, :principle2, :principle3, :principle4, :principle5 . :principle1 a :KeyPrinciple ; rdfs:label "The Customer File Depreciates; the Interest Graph Compounds" ; rdfs:comment "Identifiers expire, opt-outs accumulate, purchase history ages. Every decision trace feeds the graph, every surface migration makes it more honest. One depreciates; the other compounds."@en . :principle2 a :KeyPrinciple ; rdfs:label "Interest Is Not Intent" ; rdfs:comment "Intent expires at checkout. Interest compounds. Scoring interest rather than just intent is the difference between a customer file and a living relationship model."@en . :principle3 a :KeyPrinciple ; rdfs:label "Organize Around Topics, Not Products" ; rdfs:comment "Products come and go. Topics endure. The graph organizes around what the audience cares about — topics, interests, communities — not SKU-level product taxonomies."@en . :principle4 a :KeyPrinciple ; rdfs:label "The Loop Needs a Shared Brain" ; rdfs:comment "Paid, owned, and earned channels each optimize locally with separate logic. The customer experiences fragmentation globally. The graph is the shared brain that breaks this pattern."@en . :principle5 a :KeyPrinciple ; rdfs:label "Agents Do Not Manufacture Trust" ; rdfs:comment "As AI agents mediate discovery and purchase, brands must earn preference before the agent arrives. Without persistent preference, the brand becomes a replaceable SKU."@en . :problemSection a schema:ArticleSection ; schema:name "The Problem: The Loop Has No Shared Brain"@en ; schema:position 1 ; schema:description "Paid has a bidding brain. Owned has a CRM brain. Earned has a cultural brain. None share memory. Each team optimizes locally while the customer experiences the fragmentation globally. Three breaks: shattered attention, dead transactional loyalty, and collapsed third-party data access."@en . :speedsSection a schema:ArticleSection ; schema:name "Four Speeds of Audience Movement"@en ; schema:position 3 . :graphSection a schema:ArticleSection ; schema:name "What the Audience Interest Graph Actually Is"@en ; schema:position 4 ; schema:description "A living model of who the audience is, what they care about now, and where those interests surface. Distinct from a CDP: a CDP organizes customer records; an interest graph organizes changing relationships. Five required functions distinguish a real graph from analytics or a dashboard."@en . :agenticCommerceSection a schema:ArticleSection ; schema:name "Why Now: Agents Change the Math"@en ; schema:position 7 ; schema:description "AI agents that browse, compare, recommend, and buy make the interest graph urgent. Brands without persistent preference when the agent arrives become 'a SKU on a shelf the agent controls.' Agents do not manufacture trust — the relationship must exist beforehand."@en . :howToStartSection a schema:ArticleSection ; schema:name "How to Start"@en ; schema:position 8 ; schema:description "Seven starting points: use ignored first-party signals, ask better questions, triangulate with partners, organize around topics, score interest not just intent, decay old signals and capture decision traces, and activate with restraint."@en . :faqPage a schema:FAQPage ; schema:name "Frequently Asked Questions"@en ; schema:mainEntity :faq1, :faq2, :faq3, :faq4, :faq5, :faq6, :faq7, :faq8, :faq9, :faq10, :faq11, :faq12 . :faq1 a schema:Question ; schema:name "What is an audience interest graph?"@en ; schema:acceptedAnswer :a1 . :a1 a schema:Answer ; schema:text "A living model of who the audience is, what they care about right now, and where those interests surface. Unlike a CDP (which organizes customer records), an interest graph organizes changing relationships. It must Sense, Connect, Decay, Recommend, and Learn."@en . :faq2 a schema:Question ; schema:name "Why has the customer file stopped being enough?"@en ; schema:acceptedAnswer :a2 . :a2 a schema:Answer ; schema:text "Three reasons: attention shattered across surfaces (TikTok, Discord, podcasts, AI interfaces), transactional loyalty stopped moving the needle with younger consumers who value relevance over points, and privacy changes made third-party data expensive and unreliable."@en . :faq3 a schema:Question ; schema:name "What are the four speeds of audience movement?"@en ; schema:acceptedAnswer :a3 . :a3 a schema:Answer ; schema:text "Identity (decade-scale — who they are), Beliefs/Values (year-scale — what they stand for), Interests (week-to-month — what they care about now), and Surfaces (fastest — where they show up today). Most marketing planning still treats all four as one static thing."@en . :faq4 a schema:Question ; schema:name "What are the five functions of a real interest graph?"@en ; schema:acceptedAnswer :a4 . :a4 a schema:Answer ; schema:text "Sense (detect emerging interests), Connect (link entities into a unified graph), Decay (fade old signals — 'if it does not decay, it is already stale'), Recommend (produce actionable output), and Learn (capture decision traces to sharpen future decisions)."@en . :faq5 a schema:Question ; schema:name "How is an interest graph different from a CDP?"@en ; schema:acceptedAnswer :a5 . :a5 a schema:Answer ; schema:text "A CDP organizes customer records. An interest graph organizes changing relationships. A CDP tells you who bought what. An interest graph tells you what they care about now and where those interests surface."@en . :faq6 a schema:Question ; schema:name "What is the difference between interest and intent?"@en ; schema:acceptedAnswer :a6 . :a6 a schema:Answer ; schema:text "Intent says someone may be shopping now — it expires at checkout. Interest says this topic matters to them — it compounds over time. The graph scores interest, not just intent."@en . :faq7 a schema:Question ; schema:name "What is the 'loop with no shared brain'?"@en ; schema:acceptedAnswer :a7 . :a7 a schema:Answer ; schema:text "Paid channels have a bidding brain, owned channels a CRM brain, earned channels a cultural brain. Each optimizes locally with separate logic. The customer experiences the fragmentation globally. The interest graph is the shared brain."@en . :faq8 a schema:Question ; schema:name "Why does agentic commerce make the interest graph urgent?"@en ; schema:acceptedAnswer :a8 . :a8 a schema:Answer ; schema:text "AI agents that browse, compare, and buy on behalf of consumers will choose brands that have earned persistent preference. Without it, a brand becomes 'a SKU on a shelf the agent controls.' Agents do not manufacture trust — the relationship must exist beforehand."@en . :faq9 a schema:Question ; schema:name "What is a decision trace?"@en ; schema:acceptedAnswer :a9 . :a9 a schema:Answer ; schema:text "A record of what was decided, why, and whether it worked. Decision traces enable the graph to learn — each action sharpens the next. Without traces, decisions are isolated events rather than compounding assets."@en . :faq10 a schema:Question ; schema:name "What is the simple test for whether you have an interest graph?"@en ; schema:acceptedAnswer :a10 . :a10 a schema:Answer ; schema:text "Name the top three emerging interests among your top 10,000 customers that did not exist eighteen months ago. If your team cannot answer, you have a customer file with a marketing layer painted on top."@en . :faq11 a schema:Question ; schema:name "How does the interest graph compound?"@en ; schema:acceptedAnswer :a11 . :a11 a schema:Answer ; schema:text "Every decision trace feeds it. Every surface migration makes it more honest. Every interest signal that decays properly keeps it current. Unlike the customer file (which depreciates as identifiers expire and purchases age), the graph compounds."@en . :faq12 a schema:Question ; schema:name "How do you start building an interest graph?"@en ; schema:acceptedAnswer :a12 . :a12 a schema:Answer ; schema:text "Seven starting points: use ignored first-party signals, ask better questions, triangulate with partners, organize around topics, score interest not just intent, decay old signals and capture decision traces, and activate with restraint — interest is not intent."@en . :glossarySet a schema:DefinedTermSet ; schema:name "Glossary of Key Terms"@en ; schema:hasDefinedTerm :audienceInterestGraph, :customerFileLimits, :agenticCommerce, :interestVsIntent, :decisionTrace, :cdpTerm, :surfaceMigration, :sharedBrain . :decisionTrace a schema:DefinedTerm ; schema:name "Decision Trace"@en ; schema:description "A record of what was decided, why, and whether it worked — enabling each marketing action to sharpen the next."@en . :cdpTerm a schema:DefinedTerm ; schema:name "Customer Data Platform (CDP)"@en ; schema:description "A platform that organizes customer records — distinct from an interest graph, which organizes changing relationships rather than static transaction histories."@en . :surfaceMigration a schema:DefinedTerm ; schema:name "Surface Migration"@en ; schema:description "The movement of audiences between platforms and channels — TikTok last year, Discord this quarter. Surface migration is a signal, not noise."@en . :sharedBrain a schema:DefinedTerm ; schema:name "Shared Brain"@en ; schema:description "The interest graph functioning as unified memory across paid, owned, and earned channels — replacing the fragmentation where each channel optimizes with separate logic."@en . :howtoSection a schema:HowTo ; schema:name "How to Start Building an Audience Interest Graph"@en ; schema:description "A seven-step guide derived from Abhi Yadav's article for marketing teams beginning the shift from customer file to audience interest graph."@en ; schema:step :step1, :step2, :step3, :step4, :step5, :step6, :step7 . :step1 a schema:HowToStep ; schema:name "Use Ignored First-Party Signals"@en ; schema:position 1 ; schema:text "Mine site behavior, support transcripts, return reasons, search queries, and content engagement — signals you already collect but ignore. These reveal interests that purchase history alone cannot."@en . :step2 a schema:HowToStep ; schema:name "Ask Better Questions"@en ; schema:position 2 ; schema:text "Move beyond satisfaction scores. Ask about plans, trusted sources, wishes, and aversions. The questions you ask determine whether you learn about identity, beliefs, or interests."@en . :step3 a schema:HowToStep ; schema:name "Triangulate with Partners"@en ; schema:position 3 ; schema:text "Work with creators, retail media networks, communities, and publishers. Their signals fill gaps your first-party data cannot cover. Interest signals exist outside your owned channels."@en . :step4 a schema:HowToStep ; schema:name "Organize Around Topics, Not Products"@en ; schema:position 4 ; schema:text "Products come and go. Topics endure. Structure the graph around what the audience cares about — interests, communities, themes — not SKU-level product taxonomies."@en . :step5 a schema:HowToStep ; schema:name "Score Interest, Not Just Intent"@en ; schema:position 5 ; schema:text "Intent expires at checkout. Interest compounds. Build scoring models that distinguish between someone shopping now and someone for whom this topic matters — and weight accordingly."@en . :step6 a schema:HowToStep ; schema:name "Decay Old Signals and Capture Decision Traces"@en ; schema:position 6 ; schema:text "Old interests must fade. New decisions must leave traces. Without decay, the graph is stale. Without traces, it cannot learn. Both are non-negotiable for a living model."@en . :step7 a schema:HowToStep ; schema:name "Activate with Restraint"@en ; schema:position 7 ; schema:text "Interest is not intent. Use the graph to inform timing and relevance, not to blast every signal with a promotion. The relationship is the moat — protect it by activating with judgment."@en . :kgGeneratorSkill a schema:SoftwareApplication ; schema:name "kg-generator skill"@en ; schema:url ; schema:description "LLM-prompt-based Knowledge Graph generation skill."@en .