Typical commerce analytics
- Page viewedObserved
- Added to cartObserved
- Checkout startedObserved
- PurchaseObserved
iKawn Commerce Intelligence
Understand what customers want before they buy.
iKawn is building toward a commerce intelligence layer that turns consented virtual shopping interactions into useful signals about consideration, preference, and purchase intent.
The consideration gap
Commerce platforms know what sold.
They rarely know what almost sold.
Checkout data explains the outcome. Product interaction data can help explain the consideration journey that came before it.
With interaction context
- Product triedAvailable foundation
- Alternative comparedAvailable foundation
- Product revisitedAvailable foundation
- Intent interpretedProduct direction
From interaction to intent
The experience creates the signal.
Mirror is the current product surface. The intelligence layer is designed to make its product interactions useful beyond the individual session.
Commerce experiences
- iKawn Mirror
- Live and photo-based try-on
- Brand catalog interactions
Interaction events
- Session boundaries
- Product selections and changes
- Defined next-step handoffs
Intelligence layer
- Intent and preference models
- Product affinity
- Merchandising and retargeting signals
Product direction
See the signals traditional analytics misses.
These are the questions the intelligence layer is intended to help brands answer. They are roadmap capabilities, not claims of current automated prediction.
Product consideration
Which products earned serious exploration—not merely a page view.
Comparison behavior
Which alternatives entered the same consideration journey.
Preference signals
Which styles, colors, and products repeatedly attract attention.
Purchase intent
Patterns that may indicate high intent before conversion.
Retargeting context
Consent-aware follow-up based on real product consideration.
Merchandising insight
Products that attract curiosity but lose momentum before purchase.
One intelligence layer
Multiple commerce surfaces.
Mirror is the first product surface—not a disconnected kiosk business. The longer-term architecture is designed to bring consented interaction context together across commerce journeys.
Mirror / in-store
Web
Mobile
Consumer experiences
Developing layeriKawn Commerce Intelligence
First-party interaction data
Useful signals without invasive assumptions.
Direct product interaction
Signals begin with actions inside the brand experience: trying, switching, revisiting, or continuing.
Consent and purpose first
Profiles or retargeting require appropriate consent and a clearly defined brand use.
No identity claims from anonymous use
A try-on interaction alone does not identify an anonymous shopper.
Brand-controlled scope
Each deployment defines what is measured, retained, shared, and deleted.
Product maturity
Built in clear stages.
The roadmap is deliberately presented without speculative dates or claims of a proprietary dataset that does not yet exist.
Interaction foundation
- Live and photo-based try-on
- Catalog and product interaction
- Session and handoff design
Intent intelligence
- Intent scoring
- Preference modeling
- Product affinity
- Consent-aware retargeting signals
Cross-channel learning
- Commerce-surface intelligence
- Category benchmarking
- Market-level demand patterns