AI-Powered Personalization- D2C Fashion Brand

Client:  A D2C fashion and lifestyle brand with over 1 million monthly visitors and 10,000+ SKUs.

Category: AI/ML

Date: June 2024

Users were browsing — but not buying.

• Bounce rates were high

• Add-to-cart conversion was under 3%

• Shoppers couldn’t find what they wanted — fast enough

StyleNest’s existing product recommendations were rule-based: “Customers also bought…” — generic and outdated.
They needed something smarter, dynamic, and truly personal — to turn visitors into buyers.

We designed a real-time recommendation engine powered by machine learning, customer behavior modeling, and deep learning embeddings.

Phase 1: Understanding Shoppers:

We analyzed over 8 million events: product views, cart actions, purchase history, clickstreams, and even scroll depth.

Then we built user personas on the fly — style, price sensitivity, color preferences, sizes — and mapped them with product vectors.

Phase 2: AI Recommendation Engine:

We trained a hybrid model combining collaborative filtering + neural embeddings + popularity + context (e.g. time of day, season, weather in user’s location).

It ran in real-time, adapting with every click.

The result?

Shoppable moments” that felt tailored — like, “how did they know I’d want that?”
Phase 3: A/B Testing + Optimization:

We split test the AI engine vs. the old rule-based system for 45 days across high-traffic pages.

Client saw a 41% boost in conversions, a ₹450 increase in average order value, and a 15% drop in bounce rate. Repeat purchases rose by 9%, and overall revenue jumped 34%, all powered by real-time AI recommendations delivered in under 300ms.