Experimenting beyond optimization: How Newton Baby scaled acquisition with AI.

About

Newton Baby is a direct-to-consumer baby mattress brand dedicated to safe, breathable sleep for infants and toddlers. Their high-consideration products, such as cribs and mattresses, appeal primarily to first-time parents making one-time, high-stakes decisions. Competing in a crowded digital space, Newton Baby needed a solution that would elevate new customer acquisition without eroding ROAS or increasing acquisition costs.

In early 2024, Newton Baby partnered with NetElixir to leverage LXRInsights’ experimentation engine, designed to uncover new revenue through rapid testing and AI-powered segmentation.

The goal: acquire more high-value customers and improve the performance of paid search and social without increasing budget. 

Challenge

Traditional lookalike targeting on platforms like Google and Meta was delivering baseline results, but there was room for improvement. Newton Baby had already optimized much of its paid media strategy, their channel mix, creative, and targeting were finely tuned. That made it especially challenging for the LXRInsights team to deliver incremental impact.

The real opportunity lay in pushing beyond standard audience signals to identify nuanced behaviors that distinguish casual browsers from true high-intent buyers. For a high-consideration product like a crib, timing, trust, and contextual relevance are critical. Newton Baby needed a solution that could anticipate purchase signals—before a user even added a product to cart.

Beyond just increasing traffic, the challenge was to convert qualified, high-value prospects who were in the market but hadn’t yet chosen a brand thus unlocking new customer segments without compromising ROAS.

Solution

To help Newton Baby refine its customer acquisition strategy, our experimentation team deployed a tailored experimentation approach powered by LXRInsights. The foundation of the solution was LXRInsights' advanced AI segmentation model, which goes beyond traditional lookalike targeting by identifying distinct behavioral patterns shared by high-value customers. This allowed the team to bypass the limitations of platform-native audiences and reach prospects who closely mirrored Newton Baby’s best historical buyers.

Our experimentation team then focused on targeting new parents who exhibited strong affinities toward parenting products, particularly those signaling that they were in the active decision window for purchasing cribs and related essentials. These signals were used to build highly responsive audience segments optimized for timing and intent.

Through a series of rapid, micro-level experiments, the team tested different ad creatives, messaging frameworks, audience combinations, and keyword strategies to isolate what truly moved the needle. These experiments were structured to measure incremental lift in revenue, rather than just performance benchmarks. The combination of advanced segmentation, precision targeting, and iterative testing allowed Newton Baby to uncover actionable insights and translate them into measurable growth.