Add-to-cart rate, improving from 0.5% to over 1% on a per-user basis
By shifting algorithm configuration from developers to product, marketing, and content experts through low-code tooling
Strengthening the first-party data foundation that feeds the company's C360 platform
A global consumer goods company needed to redevelop a legacy web application that walks consumers through a quiz on hair condition, care routine, and aesthetic goals, then recommends products from a wide portfolio. The existing system used basic behavioral signals from prior visitors to profile the current shopper and suggest products with the highest likelihood of conversion, but the model was opaque, the codebase was costly to maintain, and any tuning required engineering involvement.
The company wanted a recommender that reflected both empirical conversion data and the product expertise of its in-house scientists, paired with tooling that would let marketing, content, and product teams adjust algorithm behavior without filing work into the engineering queue.
OneSix designed and built an AI product recommender that blends expert product knowledge with empirical behavioral data, including click-through rates, add-to-cart actions, and page-view times, to maximize sales conversion across the quiz platform. The model uses behavioral signals from prior visitors to profile the current shopper and surface the products most likely to convert in real time.
Around the recommender, OneSix built a low-code tuning layer purpose-built for the non-engineering teams who understand the product line and the shopper. Product scientists can tune recommendation weighting based on formulation expertise. Sales and marketing can promote new product lines without rewriting algorithm logic. Web and content management experts can configure algorithm behavior directly, shifting configuration work away from engineering.
The redesigned platform doubled the add-to-cart rate, from 0.5% to over 1% on a per-user basis, reduced ongoing maintenance costs by retiring legacy code paths and moving configuration to product experts, and increased quiz completion and email acquisition. The captured first-party data now feeds the company's C360 platform, strengthening its broader customer data foundation.
More to explore
OneSix developed a scalable horse workout tracking application using advanced computer vision, multi-camera arrays, and ultra-wideband tracking to monitor key performance metrics and detect early signs of injury.
OneSix implemented a scalable data warehouse solution for a digital health company, enabling efficient, accurate reporting and data-driven insights into member engagement across texting programs.
.avif)
OneSix designed and delivered a modern cloud governance and catalog program that retired the university's on-premise stack and embedded standardized business terms, data quality, and stewardship into day-to-day operations.