Overview
What we delivered
Discover how leveraging AI-driven recommendations and automated customer interactions led to a significant lift in conversions and average order value for a mid-size D2C brand.
Client
Mid-size D2C apparel brand in India with growing paid traffic but low conversion from product listings.
Challenge
Low add-to-cart rate and generic catalog experience caused high bounce from PLP and low PDP→checkout CTR.
Add-to-cart rate at 2.3%
Generic PLP/PDP with no personalization
No on-site search re-ranking by intent
Limited experimentation capability
Solution
Implemented AI-driven recommendations and personalization across PLP, PDP, and cart with A/B testing guardrails.
Session + history based product recommendations (home, PLP, PDP, cart)
Segments for new vs returning vs high-intent users
On-site search re-ranking based on intent and margin
Experiment framework with feature flags
Tech Stack
React/Next.js, Node.js
Python services, Redis, PostgreSQL
GA4, Segment, BigQuery for analytics
Implementation
Week 1–2: Data audit, tracking, ETL to BigQuery
Week 3–4: Recs MVP + PDP/PLP containers; flags
Week 5–6: Search re-rank, cart upsell, experiments
Results
+31% add-to-cart rate (2.3% → 3.0%)
+18% PDP→checkout CTR
+12% AOV via bundles/upsells
-9% PLP bounce
Business Impact
Improved ROAS from paid campaigns and reduced merchandising overhead due to automated recommendations.
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