Marketing Now Starts With Systems: Jiani Luo's Architecture-First Approach
In the U.S., more discovery and comparison happens through AI-assisted platforms before a buyer ever lands on your site. That shift changes the job of marketing. Jiani Luo's work pushes brands to stop thinking in campaigns and start building marketing architecture-structures that stay legible to machines and useful to people across time.
Her core idea is simple: brands are first filtered, ranked, and explained by systems. If your signals are inconsistent, unclear, or detached from actual use cases, you lose ground long before creative gets seen. Treat marketing like an operating system, not a series of stunts.
From Campaigns to Architecture
Traditional advantages-more content, more channels, short-term optimization-fade fast in an automated market. What endures is structure. Luo centers three capabilities that compound over time:
- System legibility: Your positioning must be machine-readable and unambiguous across search, marketplaces, maps, social, and review ecosystems.
- Consistent trust signals: Proof points, ratings, policies, and third-party references need to match across touchpoints so credibility builds instead of resetting.
- Decision-context fit: Tie your presence to real usage and purchase scenarios, not abstract reach. Buyers should see why you fit their moment.
Think less about "What's the next campaign?" and more about "What's the persistent structure that helps systems and buyers reach the right conclusion about us?"
Applied in the U.S. Market
Food and beverage brands such as Haidilao (U.S.) and HEYTEA (U.S.) used this approach to sharpen clarity for American consumers. Work focused on signal consistency (menus, local search details, hours, photos), category cues, and clear decision paths-moving beyond temporary traffic spikes.
In consumer goods and lifestyle, Qbedding and Maiko Matcha prioritized semantic consistency and channel coordination. The result: stronger brand recall inside AI-influenced search and recommendations, where phrasing and proof points affect ranking and match quality.
For beauty, the "I Love My Culture" initiative for WEI Beauty treated cultural positioning as a structural asset. Instead of chasing fleeting virality, the brand invested in a stable narrative and community fit that holds up across platforms and time.
Experience categories like FunZ Trampoline Park and Nova Trampoline Park focused on local signal quality, live availability cues, and review clarity. Structuring information around family decision moments (weekend plans, group events, proximity, pricing) helped lift visits and engagement at U.S. locations.
What Changes When AI Screens First
If AI helps screen, rank, and compare options, exposure alone isn't the goal. The goal is to stay consistently interpretable, credible, and context-appropriate over months, not days. AI doesn't wash out differentiation-it magnifies it. Coherent brands earn more surface area across systems; fragmented tactics decay fast.
A Practical Playbook for U.S. Marketers
- Define the semantic spine: Lock the 5-7 phrases that describe your category, use cases, and outcomes. Use them everywhere-site, profiles, marketplaces, support docs.
- Clean up entity data: Standardize brand name, categories, locations, and attributes across Google, Apple, Yelp, Amazon, social, and maps. Fix duplicates and mismatches.
- Make proof portable: Centralize ratings, certifications, reviews, and media mentions so the same proof shows up in every environment.
- Structure your site: Use clear IA, consistent naming, FAQs tied to decision moments, and schema where it helps machines parse intent. See Google's guidance on structured data: Search Central.
- Map decision paths: For each product, list the "buy moments" (occasion, budget, urgency, location). Build content, offers, and CTAs for each path.
- Stabilize brand cues: Keep visuals, tone, and promise consistent across ads, organic, profiles, and on-site. No mixed messages.
- Instrument trust: Track review velocity, response time, returns policy clarity, and third-party badges. Treat trust like a growth loop.
- Localize where it matters: Hours, pricing, menus, photos, and promotions must match local expectations. Keep them current.
- Close the loop on feedback: Feed search queries, customer support logs, and review themes back into messaging and product pages.
- Think in quarters, test in weeks: Hold the system steady; test components fast. Protect the architecture while you iterate.
What to Measure (Beyond Vanity Metrics)
- Legibility: Query coverage for your semantic spine, entity correctness across profiles, structured data health, impression share in relevant surfaces.
- Trust: Review volume and recency, aggregated rating, response rate, third-party citation growth, policy clarity scores.
- Decision fit: Conversion by scenario (occasion, use case, location), "time to first good answer" on site, repeat visit rate by intent.
- Compounding effects: Non-paid branded search over time, map pack inclusion stability, category ranking persistence.
Why Luo's Approach Works
It treats marketing as a system with memory. Signals are coherent, proof travels, and each touchpoint reinforces the last. That's how you earn durable visibility in environments run by ranking logic and recommendation loops.
If You're Building for the U.S. Market
- Stop resetting the story every quarter. Keep the core spine fixed; test around it.
- Treat every profile and listing like a landing page. Accuracy and recency matter.
- Center real buyer moments. Write for the decision, not for the algorithm alone.
- Make trust boringly consistent. Same policies, same proof, everywhere.
Next Steps
- Audit system legibility: naming, categories, structured data, and entity consistency.
- Inventory trust signals and standardize distribution across platforms.
- Map your top 3 decision scenarios and rebuild pages and offers for those paths.
If you want structured upskilling for marketing teams working with AI-led discovery, consider this certification for marketers: AI Certification for Marketing Specialists.
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