The Bear House says AI shopping agent lifted conversions 5X

Indian D2C fashion brand The Bear House said Glance’s AI shopping agent increased conversions fivefold, lifted average order value to ₹3,500 from ₹2,000 and reduced returns by 20% through virtual try-ons and complete-look recommendations.

— Source published Wed, 19 Aug, 2026, 16:04 IST · First seen Wed, 19 Aug, 2026, 16:54 IST · Source Inc42

What happened

Indian D2C fashion brand The Bear House said Glance’s AI shopping agent lifted conversions fivefold, raised average order value to ₹3,500 from ₹2,000 and

Key facts

  • Conversions increased 5X
  • Website time spent more than tripled
  • Average order value rose from ₹2,000 to ₹3,500
  • Returns reduced by 20%
  • Pre-deployment website visits lasted 30-50 seconds

Why this matters

Glance’s performance with The Bear House strengthens the strategic case for acquiring, partnering with or integrating AI-agent, virtual try-on and recommendation capabilities into apparel commerce platforms.

What to watch

  • Conversion lift remains above 2X after 60-90 days and across new versus returning customers.
  • AOV remains near or above ₹3,000 without a meaningful increase in discount rate or customer-acquisition cost.
  • Return-rate reduction is concentrated in size- and fit-sensitive categories rather than merely reflecting a shift in product mix.
  • Repeat purchase, cancellation rate, and delivery-to-return cycle indicate that higher AOV orders are durable rather than impulse bundles.
  • Glance signs comparable apparel brands or publishes broader merchant benchmarks for AI-agent performance.
  • The Bear House expands the integration, cites margin impact, or reallocates performance-marketing budget toward AI-commerce channels.
  • Expand AI-agent assortment coverage beyond top-selling SKUs, prioritizing categories with high return rates and outfit-completion potential.
  • Test AI-generated complete-look bundles, threshold-based incentives, and personalized styling to determine whether AOV gains persist without discounting.
  • Connect try-on behavior, size selections, return reasons, and post-purchase reviews to improve fit recommendations and reduce avoidable reverse-logistics costs.
  • Measure incrementality against matched non-AI traffic cohorts; separate conversion lift from channel mix, promotion exposure, and novelty effects.
  • Prepare richer product data feeds, standardized imagery, and real-time inventory availability for other agentic-shopping platforms.

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