Why your customer file is not enough. Abhi Yadav on the living model that Senses, Connects, Decays, Recommends, and Learns — replacing static transaction records with dynamic relationship intelligence.
Optimizes for auction outcomes, cost-per metrics, and impression volume. Has no memory of what the customer actually cares about beyond conversion events.
Knows who bought what and when. Sees transactions, not interests. Optimizes for retention without understanding why someone stays or leaves.
Operates on relevance, virality, and cultural timing. No connection to what paid or owned channels are doing. Three brains, no shared memory.
Who they fundamentally are. Career shifts, family formation, relocation. Changes at the pace of life stages.
What they stand for. Worldview, brand affinities, cultural alignment. Changes with cultural shifts and personal growth.
What they care about right now. Emerging hobbies, current preoccupations. Pickleball: niche to mainstream in ~18 months.
Where they show up today. TikTok last year, Discord this quarter. Surface migration is a signal, not noise.
Detect emerging interests from first-party signals.
Link people, topics, products, surfaces into a unified graph.
Fade old signals so stale interests don't drive decisions.
Produce actionable output — audience, topic, surface, message.
Capture decision traces. Each action sharpens the next.
The customer file depreciates — identifiers expire, purchases age. The interest graph compounds — every decision trace feeds it, every surface migration makes it more honest.
Intent says someone may be shopping now — it expires at checkout. Interest says this topic matters to them — it compounds. Score interest, not just intent.
Products come and go. Topics endure. Structure the graph around what the audience cares about — interests, communities, themes — not SKU taxonomies.
As AI agents mediate discovery and purchase, persistent preference is the only durable advantage. Agents do not manufacture trust — the relationship must exist before the agent arrives.
AI agents that browse, compare, recommend, and buy are arriving. Brands face a binary outcome: with persistent preference, the agent surfaces the brand as a trusted choice. Without it, the brand becomes "a SKU on a shelf the agent controls." Agents do not manufacture trust — the relationship has to exist before the agent arrives. The interest graph builds that relationship before it is needed.
Mine site behavior, support transcripts, return reasons, search queries, content engagement — signals you already collect but ignore.
Move beyond satisfaction scores. Ask about plans, trusted sources, wishes, and aversions. Better questions reveal identity, beliefs, and interests.
Work with creators, retail media networks, communities, and publishers. Their signals fill gaps your first-party data cannot cover.
Products come and go. Topics endure. Structure the graph around what the audience cares about, not SKU-level taxonomies.
Intent expires at checkout. Interest compounds. Build scoring models that distinguish between the two and weight accordingly.
Old interests must fade. New decisions must leave traces. Both are non-negotiable for a living model.
Interest is not intent. Use the graph to inform timing and relevance — not to blast every signal. The relationship is the moat.
A living model of who the audience is, what they care about right now, and where those interests surface. Unlike a CDP (customer records), it organizes changing relationships.
Attention shattered across surfaces, transactional loyalty stopped working, and privacy changes made third-party data expensive. The file shows past purchases, not current interests.
Identity (decade), Beliefs/Values (year), Interests (week-to-month), and Surfaces (fastest). The fast layers tell you what to do next. Most planning treats all four as static.
Sense (detect), Connect (link), Decay (fade), Recommend (act), Learn (sharpen). Without Recommend it is analytics. Without Learn it is a dashboard. Without Decay it is already stale.
A CDP organizes customer records — who bought what. An interest graph organizes changing relationships — what they care about now and where interests surface.
Intent says someone may be shopping now — it expires at checkout. Interest says this topic matters to them — it compounds over time.
Paid has a bidding brain, owned a CRM brain, earned a cultural brain. Each optimizes locally. The customer experiences fragmentation globally. The graph is the shared brain.
AI agents will choose brands with 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.
A record of what was decided, why, and whether it worked. Decision traces enable the graph to learn — each action sharpens the next rather than being an isolated event.
Name the top three emerging interests among your top 10,000 customers that didn't exist 18 months ago. If you can't answer, it's a customer file with marketing paint.
Every decision trace feeds it. Every surface migration makes it more honest. Every properly decayed signal keeps it current. Unlike the depreciating customer file.
Use ignored first-party signals, ask better questions, triangulate with partners, organize around topics, score interest (not just intent), decay old signals, capture decision traces, and activate with restraint.
A living model of who the audience is, what they care about now, and where those interests surface — replacing static customer files.
The structural ceiling of transaction-record marketing — past purchases cannot predict current interests or surface behavior.
A record of what was decided, why, and whether it worked — enabling each action to sharpen the next.
Customer Data Platform that organizes records — distinct from a graph that organizes changing relationships.
Audience movement between platforms — TikTok last year, Discord this quarter. A signal, not noise.
The graph functioning as unified memory across paid, owned, and earned channels — breaking the fragmentation loop.
Intent expires at checkout. Interest compounds. The graph scores interest, not just intent.
AI agents browsing, comparing, and buying on behalf of consumers — making persistent brand preference non-optional.