Heads Up AEO for Meta Ray-Ban Display: A Playbook for On-Lens Revenue

Why this matters now

Meta announced Ray-Ban Display on September 18, 2025, with U.S. retail slated for September 30. It is the first Ray-Ban with a built-in display and Neural Band input, which shifts answers from pages to on-lens steps. See the context in this Meta Ray-Ban Display announcement coverage.

What Heads Up AEO is

Answer Engine Optimization for heads-up displays focuses on rendering content as on-lens instructions rather than web pages.

• No SERP and no typing
• Near-zero friction with glanceable steps
• Consent-first follow-ups for conversion

What to ship

• Step-by-step microcards at 5 to 9 words each
• High-contrast icon set for each task state
• Geo anchors for store aisles and zones
• SKU tags and variants for exact matches
• WhatsApp opt-in for follow-ups and checkout

Microcard patterns

Recipes

• Preheat oven to 400 F
• Rinse chicken and pat dry
• Rub spice blend evenly all sides
• Insert probe center of thickest part
• Rest ten minutes before slicing

Repairs

• Power off and unplug device
• Remove back plate two Phillips screws
• Lift battery tab gently, do not pry
• Seat new battery, align gold contacts
• Run diagnostics, confirm green LED

In-store wayfinding

• You are near Aisle B4 beverages
• Walk 60 feet to endcap left
• Pick 12 ounce bottle, lemon flavor
• Compare unit price per ounce
• Tap band to save receipt

Design rules

• 5 to 9 words per card using active verbs
• One icon per card, maximum 2 colors plus white
• Avoid text above eye-level glare zones
• Provide left-right variants for monocular layouts
• Add haptics or tone for step change

Data model

Recommended fields for each card and session:

• card_id, intent, text, icon_id, sku, sku_variant
• geo_anchor_id, next_card_id, timeout_ms, cta_type
• Geo anchor types: aisle, bay, endcap, service desk, curbside
• SKU tags: UPC, GTIN, size, pack, flavor, color

Example payload

{
  "intent": "recipe",
  "sequence": [
    {"card_id": "c1", "text": "Preheat oven to 400 F", "icon_id": "heat", "timeout_ms": 8000},
    {"card_id": "c2", "text": "Rinse chicken and pat dry", "icon_id": "wash", "timeout_ms": 10000}
  ],
  "handoff": {
    "channel": "whatsapp",
    "template_id": "optin_recipe",
    "consent_required": true
  }
}

Handoff to WhatsApp

• Present a single-tap opt-in card after the final step
• Prefill the thread with a saved list or cart
• Use product messages with SKU tags for checkout
• Respect frequency caps and quiet hours

Ranking signals for on-lens answers

• First glance success rate within 3 seconds
• Card completion rate without backtracking
• Geo precision within 3 meters in store
• SKU match rate versus inventory API
• Opt-in rate and assisted conversion rate

Instrumentation

• Log card impressions, dwell, completes, and abandons
• Capture band gestures and voice intents
• Store geo anchor accuracy and latency
• Append WhatsApp message IDs to the session

Governance

• Consent-first opt-ins with a clear value exchange
• Keep sensitive steps on-device when possible
• Minimize PII in on-lens text
• Rotate icons during accessibility testing

Pilot plan weeks 1 to 4

• Week 1 map intents and draft microcards
• Week 2 build the icon set and data model
• Week 3 complete in-store geo anchoring and QA
• Week 4 soft launch plus WhatsApp templates

Team checklist

• Content: writers create microcards and variants
• Design: HUD icons and contrast testing
• Data: SKU normalization and store maps
• Eng: API for intent to card rendering
• CRM: WhatsApp templates and consent flows

North star metric

Glance to conversion time under 90 seconds.

Related playbooks

• Learn how to make products camera clickable to align your microcards with visual shopping.
• Use tab context to earn in-browser citations without SERPs that support on-lens discovery.
• Expand discovery with session tactics from win discovery in Gemini Mode.