AI ads and the next era of consumer value
Advertising inside AI assistants isn't the end of a clean experience. It's the start of a smarter one. Relevance and trust beat interruption. That's the shift.
OpenAI is exploring ads inside ChatGPT, and the model is cost-per-view. Translation: fewer click-out traps, more on-the-spot utility. For marketers, this moves the game from grabbing attention to earning it.
From interruption to intent
Search is passive. Chat is active. People arrive with goals: compare, decide, buy, fix.
Example: someone asks about long-distance running shoes. The assistant breaks down cushioning, drop, weight, and fit. A timely promo, price check, or local availability slot can help them decide faster. That's not disruption. That's service.
What "good" looks like inside AI experiences
- Context-aware: Creative adapts to the question, stage, and constraints (budget, size, time).
- Utility-first: Offers that reduce steps: price comparisons, in-stock alerts, fit guidance, bundle logic.
- Non-intrusive: Inline modules that add clarity, not clutter. No autoplay, no surprise redirects.
- Choice-preserving: Clear labels. Easy dismiss. No dark patterns.
- Structured by default: Product, promo, and policy data formatted for LLMs to read and render.
Trust is the moat
None of this works without clear labeling and transparent logic. People need to know what's sponsored, why it's shown, and what signals informed it.
Set and uphold standards: consistent disclosures, explainable ranking, and respect for data boundaries. If you want a baseline, study regulator guidance on native ad disclosures from the FTC.
What marketers should do next
- Map user intent: Build an "intent library" for your category (research, compare, validate, buy, troubleshoot). Link each intent to useful ad responses.
- Structure your data: Feed clean product feeds, availability, pricing, reviews, and store info in LLM-friendly formats. Keep it fresh.
- Design modular creative: Short, swappable components: value prop, social proof, offer, local signal, action. Keep variants tight.
- Rethink incentives: Optimize for assists, not exits. View-through attention and decision quality beat raw clicks.
- Set guardrails: Brand prompts, banned claims, response tone, and context filters. Audit regularly.
- Close the loop: Pass back outcomes (save, add-to-cart, store visit, lead quality) to improve relevance.
Measurement that respects the chat
- Primary metrics: Cost per view, qualified consideration rate, time-to-decision, and assist rate.
- Quality signals: Saves, compares, configurator usage, store pickup checks, and post-chat conversion.
- Incrementality: Geo or time-based tests. Lightweight MMM that includes "assist" events.
- Attribution sanity: Credit the conversation, not the last click. Build shared KPIs with product and growth.
Creative that fits the conversation
- Write like an assistant: Clear, helpful, concise. No hype.
- Anticipate follow-ups: Add quick branches: "compare X vs Y," "show sizes in stock," "nearest pickup."
- Lean on proof: Ratings, expert quotes, return policy, warranty. Keep it verifiable.
- Respect the context: If the user is problem-solving, lead with relief and clarity. If they're checking out, lead with certainty and speed.
Pitfalls to avoid
- Click-out bait that breaks the flow.
- Opaque targeting or vague disclosures.
- Generic banners recycled from display.
- One-size creative that ignores intent.
- Privacy shortcuts that erode trust.
The bigger picture
Advertising funds access. AI doesn't change that. What changes is the standard for usefulness.
If ads help people decide faster with less friction, they win. Keep it honest, relevant, and lightweight-and you'll earn attention without taking it.
Level up your team
If you're building AI-ready media and creative workflows, explore practical training for marketers here: AI Certification for Marketing Specialists.
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