AI-Powered Retail Personalization Engine
A direct-to-consumer apparel brand with annual revenue of $120 million was using basic segment-based personalization: showing the same homepage banner to all women aged 25-40, for example. Their email campaigns had a 14% open rate and their product recommendation widget was powered by a simple 'also bought' algorithm that ignored context, seasonality and individual preferences. Customers frequently complained about irrelevant recommendations, and the site's average order value had plateaued for 18 months.
Challenge
A direct-to-consumer apparel brand with annual revenue of $120 million was using basic segment-based personalization: showing the same homepage banner to all women aged 25-40, for example. Their email campaigns had a 14% open rate and their product recommendation widget was powered by a simple 'also bought' algorithm that ignored context, seasonality and individual preferences. Customers frequently complained about irrelevant recommendations, and the site's average order value had plateaued for 18 months.
Solution
We built a real-time personalization engine that processed customer behavior across web sessions, email clicks, purchase history, returns data and browse patterns. The system used collaborative filtering and a two-tower neural network to generate product embeddings, then served personalized recommendations for the homepage, search results, product pages and email campaigns via a low-latency API. We also built an experimentation framework that let the marketing team A/B test personalization strategies without engineering involvement. The system learned from every interaction and updated user profiles within seconds.
Outcome
Average order value increased 23% within three months. Email open rates rose from 14% to 31%. Revenue per visitor increased 41% for users who received personalized recommendations. The marketing team launched 12 A/B experiments in the first month without writing any code. The platform now serves 3 million personalized requests per day with p99 latency under 80 milliseconds.
Technology Stack
Architecture
Event Stream → Feature Store → ML Inference → Experimentation API → Frontend SDKsReady to Build Something Exceptional?
Let's discuss how Aloha Studio can help you design, engineer and scale your next digital product.